id	sid	tid	token	lemma	pos
iajs-3728	1	1	325	325	NUM
iajs-3728	1	2	©	©	PROPN
iajs-3728	1	3	2025	2025	NUM
iajs-3728	1	4	the	the	DET
iajs-3728	1	5	author(s	author(s	NOUN
iajs-3728	1	6	)	)	PUNCT
iajs-3728	1	7	.	.	PUNCT
iajs-3728	2	1	published	publish	VERB
iajs-3728	2	2	by	by	ADP
iajs-3728	2	3	college	college	NOUN
iajs-3728	2	4	of	of	ADP
iajs-3728	2	5	education	education	NOUN
iajs-3728	2	6	for	for	ADP
iajs-3728	2	7	pure	pure	ADJ
iajs-3728	2	8	science	science	NOUN
iajs-3728	2	9	(	(	PUNCT
iajs-3728	2	10	ibn	ibn	PROPN
iajs-3728	2	11	al	al	PROPN
iajs-3728	2	12	-	-	PUNCT
iajs-3728	2	13	haitham	haitham	PROPN
iajs-3728	2	14	)	)	PUNCT
iajs-3728	2	15	,	,	PUNCT
iajs-3728	2	16	university	university	NOUN
iajs-3728	2	17	of	of	ADP
iajs-3728	2	18	baghdad	baghdad	PROPN
iajs-3728	2	19	.	.	PUNCT
iajs-3728	3	1	this	this	PRON
iajs-3728	3	2	is	be	AUX
iajs-3728	3	3	an	an	DET
iajs-3728	3	4	open	open	ADJ
iajs-3728	3	5	-	-	PUNCT
iajs-3728	3	6	access	access	NOUN
iajs-3728	3	7	article	article	NOUN
iajs-3728	3	8	distributed	distribute	VERB
iajs-3728	3	9	under	under	ADP
iajs-3728	3	10	the	the	DET
iajs-3728	3	11	terms	term	NOUN
iajs-3728	3	12	of	of	ADP
iajs-3728	3	13	the	the	DET
iajs-3728	3	14	creative	creative	ADJ
iajs-3728	3	15	commons	common	NOUN
iajs-3728	3	16	attribution	attribution	NOUN
iajs-3728	3	17	4.0	4.0	NUM
iajs-3728	3	18	international	international	ADJ
iajs-3728	3	19	license	license	NOUN
iajs-3728	3	20	improving	improve	VERB
iajs-3728	3	21	modularity	modularity	NOUN
iajs-3728	3	22	regularization	regularization	NOUN
iajs-3728	3	23	techniques	technique	NOUN
iajs-3728	3	24	for	for	ADP
iajs-3728	3	25	estimating	estimate	VERB
iajs-3728	3	26	structures	structure	NOUN
iajs-3728	3	27	in	in	ADP
iajs-3728	3	28	complex	complex	ADJ
iajs-3728	3	29	network	network	NOUN
iajs-3728	3	30	samaa	samaa	PROPN
iajs-3728	3	31	f.	f.	PROPN
iajs-3728	3	32	ibraheem	ibraheem	PROPN
iajs-3728	3	33	1	1	NUM
iajs-3728	3	34	*	*	PUNCT
iajs-3728	3	35	and	and	CCONJ
iajs-3728	3	36	basad	basad	PROPN
iajs-3728	3	37	al	al	PROPN
iajs-3728	3	38	-	-	PUNCT
iajs-3728	3	39	sarray	sarray	PROPN
iajs-3728	3	40	2	2	NUM
iajs-3728	3	41	1	1	NUM
iajs-3728	3	42	department	department	NOUN
iajs-3728	3	43	of	of	ADP
iajs-3728	3	44	mathematics	mathematic	NOUN
iajs-3728	3	45	,	,	PUNCT
iajs-3728	3	46	college	college	NOUN
iajs-3728	3	47	of	of	ADP
iajs-3728	3	48	applied	apply	VERB
iajs-3728	3	49	science	science	NOUN
iajs-3728	3	50	,	,	PUNCT
iajs-3728	3	51	university	university	NOUN
iajs-3728	3	52	of	of	ADP
iajs-3728	3	53	technology	technology	NOUN
iajs-3728	3	54	,	,	PUNCT
iajs-3728	3	55	baghdad	baghdad	PROPN
iajs-3728	3	56	,	,	PUNCT
iajs-3728	3	57	iraq	iraq	PROPN
iajs-3728	3	58	.	.	PUNCT
iajs-3728	4	1	2	2	NUM
iajs-3728	4	2	department	department	NOUN
iajs-3728	4	3	of	of	ADP
iajs-3728	4	4	computer	computer	NOUN
iajs-3728	4	5	science	science	NOUN
iajs-3728	4	6	,	,	PUNCT
iajs-3728	4	7	college	college	NOUN
iajs-3728	4	8	of	of	ADP
iajs-3728	4	9	science	science	NOUN
iajs-3728	4	10	,	,	PUNCT
iajs-3728	4	11	university	university	NOUN
iajs-3728	4	12	of	of	ADP
iajs-3728	4	13	baghdad	baghdad	PROPN
iajs-3728	4	14	,	,	PUNCT
iajs-3728	4	15	baghdad	baghdad	PROPN
iajs-3728	4	16	,	,	PUNCT
iajs-3728	4	17	iraq	iraq	PROPN
iajs-3728	4	18	.	.	PUNCT
iajs-3728	5	1	*	*	PUNCT
iajs-3728	5	2	corresponding	correspond	VERB
iajs-3728	5	3	author	author	NOUN
iajs-3728	5	4	.	.	PUNCT
iajs-3728	6	1	received	receive	VERB
iajs-3728	6	2	:	:	PUNCT
iajs-3728	6	3	17	17	NUM
iajs-3728	6	4	september	september	PROPN
iajs-3728	6	5	2023	2023	NUM
iajs-3728	6	6	accepted	accept	VERB
iajs-3728	6	7	:	:	PUNCT
iajs-3728	6	8	19	19	NUM
iajs-3728	6	9	february	february	NOUN
iajs-3728	6	10	2024	2024	NUM
iajs-3728	6	11	published	publish	VERB
iajs-3728	6	12	:	:	PUNCT
iajs-3728	6	13	20	20	NUM
iajs-3728	6	14	july	july	PROPN
iajs-3728	6	15	2025	2025	NUM
iajs-3728	6	16	doi.org/10.30526/38.3.3728	doi.org/10.30526/38.3.3728	NOUN
iajs-3728	6	17	abstract	abstract	ADJ
iajs-3728	6	18	complex	complex	ADJ
iajs-3728	6	19	systems	system	NOUN
iajs-3728	6	20	in	in	ADP
iajs-3728	6	21	the	the	DET
iajs-3728	6	22	real	real	ADJ
iajs-3728	6	23	world	world	NOUN
iajs-3728	6	24	have	have	VERB
iajs-3728	6	25	networks	network	NOUN
iajs-3728	6	26	differ	differ	VERB
iajs-3728	6	27	significantly	significantly	ADV
iajs-3728	6	28	from	from	ADP
iajs-3728	6	29	random	random	ADJ
iajs-3728	6	30	graphs	graph	NOUN
iajs-3728	6	31	and	and	CCONJ
iajs-3728	6	32	have	have	VERB
iajs-3728	6	33	non	non	ADJ
iajs-3728	6	34	-	-	ADJ
iajs-3728	6	35	trivial	trivial	ADJ
iajs-3728	6	36	structures	structure	NOUN
iajs-3728	6	37	.	.	PUNCT
iajs-3728	7	1	in	in	ADP
iajs-3728	7	2	fact	fact	NOUN
iajs-3728	7	3	,	,	PUNCT
iajs-3728	7	4	they	they	PRON
iajs-3728	7	5	have	have	VERB
iajs-3728	7	6	a	a	DET
iajs-3728	7	7	community	community	NOUN
iajs-3728	7	8	structure	structure	NOUN
iajs-3728	7	9	that	that	PRON
iajs-3728	7	10	needs	need	VERB
iajs-3728	7	11	to	to	PART
iajs-3728	7	12	be	be	AUX
iajs-3728	7	13	recognized	recognize	VERB
iajs-3728	7	14	and	and	CCONJ
iajs-3728	7	15	recovered	recover	VERB
iajs-3728	7	16	.	.	PUNCT
iajs-3728	8	1	the	the	DET
iajs-3728	8	2	stochastic	stochastic	ADJ
iajs-3728	8	3	block	block	NOUN
iajs-3728	8	4	models	model	NOUN
iajs-3728	8	5	(	(	PUNCT
iajs-3728	8	6	sbms	sbms	ADJ
iajs-3728	8	7	)	)	PUNCT
iajs-3728	8	8	are	be	AUX
iajs-3728	8	9	popular	popular	ADJ
iajs-3728	8	10	models	model	NOUN
iajs-3728	8	11	for	for	ADP
iajs-3728	8	12	community	community	NOUN
iajs-3728	8	13	detection	detection	NOUN
iajs-3728	8	14	in	in	ADP
iajs-3728	8	15	networks	network	NOUN
iajs-3728	8	16	,	,	PUNCT
iajs-3728	8	17	where	where	SCONJ
iajs-3728	8	18	nodes	node	NOUN
iajs-3728	8	19	are	be	AUX
iajs-3728	8	20	divided	divide	VERB
iajs-3728	8	21	into	into	ADP
iajs-3728	8	22	groups	group	NOUN
iajs-3728	8	23	based	base	VERB
iajs-3728	8	24	on	on	ADP
iajs-3728	8	25	their	their	PRON
iajs-3728	8	26	connectivity	connectivity	NOUN
iajs-3728	8	27	patterns	pattern	NOUN
iajs-3728	8	28	.	.	PUNCT
iajs-3728	9	1	maximum	maximum	ADJ
iajs-3728	9	2	likelihood	likelihood	NOUN
iajs-3728	9	3	estimation	estimation	NOUN
iajs-3728	9	4	is	be	AUX
iajs-3728	9	5	a	a	DET
iajs-3728	9	6	common	common	ADJ
iajs-3728	9	7	method	method	NOUN
iajs-3728	9	8	for	for	ADP
iajs-3728	9	9	estimating	estimate	VERB
iajs-3728	9	10	the	the	DET
iajs-3728	9	11	parameters	parameter	NOUN
iajs-3728	9	12	of	of	ADP
iajs-3728	9	13	sbms	sbms	NOUN
iajs-3728	9	14	.	.	PUNCT
iajs-3728	10	1	in	in	ADP
iajs-3728	10	2	this	this	DET
iajs-3728	10	3	paper	paper	NOUN
iajs-3728	10	4	,	,	PUNCT
iajs-3728	10	5	a	a	DET
iajs-3728	10	6	model	model	NOUN
iajs-3728	10	7	selection	selection	NOUN
iajs-3728	10	8	for	for	ADP
iajs-3728	10	9	stochastic	stochastic	ADJ
iajs-3728	10	10	block	block	NOUN
iajs-3728	10	11	models	model	NOUN
iajs-3728	10	12	is	be	AUX
iajs-3728	10	13	presented	present	VERB
iajs-3728	10	14	based	base	VERB
iajs-3728	10	15	on	on	ADP
iajs-3728	10	16	the	the	DET
iajs-3728	10	17	optimization	optimization	NOUN
iajs-3728	10	18	of	of	ADP
iajs-3728	10	19	the	the	DET
iajs-3728	10	20	log	log	NOUN
iajs-3728	10	21	-	-	PUNCT
iajs-3728	10	22	likelihood	likelihood	NOUN
iajs-3728	10	23	function	function	NOUN
iajs-3728	10	24	to	to	PART
iajs-3728	10	25	find	find	VERB
iajs-3728	10	26	the	the	DET
iajs-3728	10	27	best	good	ADJ
iajs-3728	10	28	number	number	NOUN
iajs-3728	10	29	of	of	ADP
iajs-3728	10	30	communities	community	NOUN
iajs-3728	10	31	k	k	PROPN
iajs-3728	10	32	detected	detect	VERB
iajs-3728	10	33	by	by	ADP
iajs-3728	10	34	the	the	DET
iajs-3728	10	35	regularized	regularize	VERB
iajs-3728	10	36	convex	convex	NOUN
iajs-3728	10	37	modularity	modularity	NOUN
iajs-3728	10	38	maximization	maximization	NOUN
iajs-3728	10	39	method	method	NOUN
iajs-3728	10	40	.	.	PUNCT
iajs-3728	11	1	this	this	DET
iajs-3728	11	2	work	work	NOUN
iajs-3728	11	3	deals	deal	VERB
iajs-3728	11	4	with	with	ADP
iajs-3728	11	5	many	many	ADJ
iajs-3728	11	6	assumptions	assumption	NOUN
iajs-3728	11	7	on	on	ADP
iajs-3728	11	8	k	k	PROPN
iajs-3728	11	9	because	because	SCONJ
iajs-3728	11	10	it	it	PRON
iajs-3728	11	11	is	be	AUX
iajs-3728	11	12	necessary	necessary	ADJ
iajs-3728	11	13	to	to	PART
iajs-3728	11	14	study	study	VERB
iajs-3728	11	15	the	the	DET
iajs-3728	11	16	behavior	behavior	NOUN
iajs-3728	11	17	of	of	ADP
iajs-3728	11	18	the	the	DET
iajs-3728	11	19	network	network	NOUN
iajs-3728	11	20	and	and	CCONJ
iajs-3728	11	21	the	the	DET
iajs-3728	11	22	best	good	ADJ
iajs-3728	11	23	optimum	optimum	NOUN
iajs-3728	11	24	k	k	PROPN
iajs-3728	11	25	is	be	AUX
iajs-3728	11	26	assumed	assume	VERB
iajs-3728	11	27	to	to	PART
iajs-3728	11	28	select	select	VERB
iajs-3728	11	29	the	the	DET
iajs-3728	11	30	best	good	ADJ
iajs-3728	11	31	partition	partition	NOUN
iajs-3728	11	32	over	over	ADP
iajs-3728	11	33	the	the	DET
iajs-3728	11	34	aic	aic	PROPN
iajs-3728	11	35	,	,	PUNCT
iajs-3728	11	36	bic	bic	PROPN
iajs-3728	11	37	metric	metric	NOUN
iajs-3728	11	38	.	.	PUNCT
iajs-3728	12	1	the	the	DET
iajs-3728	12	2	proposed	propose	VERB
iajs-3728	12	3	model	model	NOUN
iajs-3728	12	4	selection	selection	NOUN
iajs-3728	12	5	method	method	NOUN
iajs-3728	12	6	is	be	AUX
iajs-3728	12	7	presented	present	VERB
iajs-3728	12	8	in	in	ADP
iajs-3728	12	9	an	an	DET
iajs-3728	12	10	algorithm	algorithm	NOUN
iajs-3728	12	11	that	that	PRON
iajs-3728	12	12	is	be	AUX
iajs-3728	12	13	implemented	implement	VERB
iajs-3728	12	14	for	for	ADP
iajs-3728	12	15	both	both	CCONJ
iajs-3728	12	16	real	real	ADJ
iajs-3728	12	17	and	and	CCONJ
iajs-3728	12	18	synthetic	synthetic	ADJ
iajs-3728	12	19	networks	network	NOUN
iajs-3728	12	20	.	.	PUNCT
iajs-3728	13	1	this	this	DET
iajs-3728	13	2	method	method	NOUN
iajs-3728	13	3	enables	enable	VERB
iajs-3728	13	4	the	the	DET
iajs-3728	13	5	detection	detection	NOUN
iajs-3728	13	6	of	of	ADP
iajs-3728	13	7	networks	network	NOUN
iajs-3728	13	8	with	with	ADP
iajs-3728	13	9	small	small	ADJ
iajs-3728	13	10	communities	community	NOUN
iajs-3728	13	11	that	that	PRON
iajs-3728	13	12	are	be	AUX
iajs-3728	13	13	more	more	ADV
iajs-3728	13	14	likely	likely	ADJ
iajs-3728	13	15	to	to	PART
iajs-3728	13	16	provide	provide	VERB
iajs-3728	13	17	a	a	DET
iajs-3728	13	18	better	well	ADJ
iajs-3728	13	19	fit	fit	NOUN
iajs-3728	13	20	to	to	ADP
iajs-3728	13	21	the	the	DET
iajs-3728	13	22	observed	observed	ADJ
iajs-3728	13	23	data	datum	NOUN
iajs-3728	13	24	.	.	PUNCT
iajs-3728	14	1	keywords	keyword	NOUN
iajs-3728	14	2	:	:	PUNCT
iajs-3728	14	3	community	community	NOUN
iajs-3728	14	4	detection	detection	NOUN
iajs-3728	14	5	,	,	PUNCT
iajs-3728	14	6	likelihood	likelihood	NOUN
iajs-3728	14	7	function	function	NOUN
iajs-3728	14	8	,	,	PUNCT
iajs-3728	14	9	model	model	NOUN
iajs-3728	14	10	selection	selection	NOUN
iajs-3728	14	11	,	,	PUNCT
iajs-3728	14	12	bayesian	bayesian	NOUN
iajs-3728	14	13	information	information	NOUN
iajs-3728	14	14	criterion	criterion	NOUN
iajs-3728	14	15	,	,	PUNCT
iajs-3728	14	16	stochastic	stochastic	ADJ
iajs-3728	14	17	block	block	NOUN
iajs-3728	14	18	models	model	NOUN
iajs-3728	14	19	.	.	PUNCT
iajs-3728	15	1	1	1	X
iajs-3728	15	2	.	.	X
iajs-3728	15	3	introduction	introduction	NOUN
iajs-3728	15	4	the	the	DET
iajs-3728	15	5	stochastic	stochastic	ADJ
iajs-3728	15	6	block	block	NOUN
iajs-3728	15	7	model	model	NOUN
iajs-3728	15	8	(	(	PUNCT
iajs-3728	15	9	sbm	sbm	NOUN
iajs-3728	15	10	)	)	PUNCT
iajs-3728	15	11	in	in	ADP
iajs-3728	15	12	its	its	PRON
iajs-3728	15	13	standard	standard	ADJ
iajs-3728	15	14	form	form	NOUN
iajs-3728	15	15	is	be	AUX
iajs-3728	15	16	a	a	DET
iajs-3728	15	17	popular	popular	ADJ
iajs-3728	15	18	tool	tool	NOUN
iajs-3728	15	19	for	for	ADP
iajs-3728	15	20	detecting	detect	VERB
iajs-3728	15	21	communities	community	NOUN
iajs-3728	15	22	in	in	ADP
iajs-3728	15	23	networks	network	NOUN
iajs-3728	15	24	.	.	PUNCT
iajs-3728	16	1	it	it	PRON
iajs-3728	16	2	is	be	AUX
iajs-3728	16	3	based	base	VERB
iajs-3728	16	4	on	on	ADP
iajs-3728	16	5	the	the	DET
iajs-3728	16	6	assumption	assumption	NOUN
iajs-3728	16	7	that	that	SCONJ
iajs-3728	16	8	the	the	DET
iajs-3728	16	9	nodes	node	NOUN
iajs-3728	16	10	in	in	ADP
iajs-3728	16	11	a	a	DET
iajs-3728	16	12	network	network	NOUN
iajs-3728	16	13	can	can	AUX
iajs-3728	16	14	be	be	AUX
iajs-3728	16	15	divided	divide	VERB
iajs-3728	16	16	into	into	ADP
iajs-3728	16	17	groups	group	NOUN
iajs-3728	16	18	or	or	CCONJ
iajs-3728	16	19	blocks	block	NOUN
iajs-3728	16	20	,	,	PUNCT
iajs-3728	16	21	so	so	SCONJ
iajs-3728	16	22	that	that	SCONJ
iajs-3728	16	23	the	the	DET
iajs-3728	16	24	probability	probability	NOUN
iajs-3728	16	25	of	of	ADP
iajs-3728	16	26	an	an	DET
iajs-3728	16	27	edge	edge	NOUN
iajs-3728	16	28	between	between	ADP
iajs-3728	16	29	two	two	NUM
iajs-3728	16	30	nodes	node	NOUN
iajs-3728	16	31	depends	depend	VERB
iajs-3728	16	32	only	only	ADV
iajs-3728	16	33	on	on	ADP
iajs-3728	16	34	which	which	DET
iajs-3728	16	35	block	block	NOUN
iajs-3728	16	36	they	they	PRON
iajs-3728	16	37	belong	belong	VERB
iajs-3728	16	38	to	to	ADP
iajs-3728	16	39	.	.	PUNCT
iajs-3728	17	1	this	this	PRON
iajs-3728	17	2	enables	enable	VERB
iajs-3728	17	3	the	the	DET
iajs-3728	17	4	estimation	estimation	NOUN
iajs-3728	17	5	of	of	ADP
iajs-3728	17	6	block	block	NOUN
iajs-3728	17	7	membership	membership	NOUN
iajs-3728	17	8	and	and	CCONJ
iajs-3728	17	9	the	the	DET
iajs-3728	17	10	detection	detection	NOUN
iajs-3728	17	11	of	of	ADP
iajs-3728	17	12	communities	community	NOUN
iajs-3728	17	13	within	within	ADP
iajs-3728	17	14	a	a	DET
iajs-3728	17	15	network	network	NOUN
iajs-3728	17	16	(	(	PUNCT
iajs-3728	17	17	1	1	NUM
iajs-3728	17	18	)	)	PUNCT
iajs-3728	17	19	.	.	PUNCT
iajs-3728	18	1	the	the	DET
iajs-3728	18	2	sbm	sbm	PROPN
iajs-3728	18	3	has	have	AUX
iajs-3728	18	4	been	be	AUX
iajs-3728	18	5	used	use	VERB
iajs-3728	18	6	to	to	PART
iajs-3728	18	7	detect	detect	VERB
iajs-3728	18	8	communities	community	NOUN
iajs-3728	18	9	in	in	ADP
iajs-3728	18	10	social	social	ADJ
iajs-3728	18	11	networks	network	NOUN
iajs-3728	18	12	,	,	PUNCT
iajs-3728	18	13	biological	biological	ADJ
iajs-3728	18	14	networks	network	NOUN
iajs-3728	18	15	and	and	CCONJ
iajs-3728	18	16	other	other	ADJ
iajs-3728	18	17	types	type	NOUN
iajs-3728	18	18	of	of	ADP
iajs-3728	18	19	networks	network	NOUN
iajs-3728	18	20	(	(	PUNCT
iajs-3728	18	21	2	2	NUM
iajs-3728	18	22	)	)	PUNCT
iajs-3728	18	23	and	and	CCONJ
iajs-3728	18	24	(	(	PUNCT
iajs-3728	18	25	3	3	NUM
iajs-3728	18	26	)	)	PUNCT
iajs-3728	18	27	.	.	PUNCT
iajs-3728	19	1	more	more	ADV
iajs-3728	19	2	specifically	specifically	ADV
iajs-3728	19	3	,	,	PUNCT
iajs-3728	19	4	it	it	PRON
iajs-3728	19	5	can	can	AUX
iajs-3728	19	6	be	be	AUX
iajs-3728	19	7	used	use	VERB
iajs-3728	19	8	to	to	PART
iajs-3728	19	9	estimate	estimate	VERB
iajs-3728	19	10	the	the	DET
iajs-3728	19	11	number	number	NOUN
iajs-3728	19	12	of	of	ADP
iajs-3728	19	13	communities	community	NOUN
iajs-3728	19	14	in	in	ADP
iajs-3728	19	15	a	a	DET
iajs-3728	19	16	network	network	NOUN
iajs-3728	19	17	as	as	ADV
iajs-3728	19	18	well	well	ADV
iajs-3728	19	19	as	as	ADP
iajs-3728	19	20	their	their	PRON
iajs-3728	19	21	size	size	NOUN
iajs-3728	19	22	and	and	CCONJ
iajs-3728	19	23	connectivity	connectivity	NOUN
iajs-3728	19	24	patterns	pattern	NOUN
iajs-3728	19	25	.	.	PUNCT
iajs-3728	20	1	in	in	ADP
iajs-3728	20	2	addition	addition	NOUN
iajs-3728	20	3	,	,	PUNCT
iajs-3728	20	4	it	it	PRON
iajs-3728	20	5	can	can	AUX
iajs-3728	20	6	be	be	AUX
iajs-3728	20	7	used	use	VERB
iajs-3728	20	8	to	to	PART
iajs-3728	20	9	identify	identify	VERB
iajs-3728	20	10	outliers	outlier	NOUN
iajs-3728	20	11	or	or	CCONJ
iajs-3728	20	12	anomalous	anomalous	ADJ
iajs-3728	20	13	nodes	node	NOUN
iajs-3728	20	14	that	that	PRON
iajs-3728	20	15	do	do	AUX
iajs-3728	20	16	not	not	PART
iajs-3728	20	17	fit	fit	VERB
iajs-3728	20	18	into	into	ADP
iajs-3728	20	19	any	any	PRON
iajs-3728	20	20	of	of	ADP
iajs-3728	20	21	the	the	DET
iajs-3728	20	22	detected	detect	VERB
iajs-3728	20	23	clusters	cluster	NOUN
iajs-3728	20	24	(	(	PUNCT
iajs-3728	20	25	4	4	NUM
iajs-3728	20	26	)	)	PUNCT
iajs-3728	20	27	.	.	PUNCT
iajs-3728	21	1	https://orcid.org/0000-0002-0836-5173	https://orcid.org/0000-0002-0836-5173	PROPN
iajs-3728	21	2	mailto:samaa.f.ibraheem@uotechnology.edu.iq	mailto:samaa.f.ibraheem@uotechnology.edu.iq	PROPN
iajs-3728	21	3	https://orcid.org/0000-0003-2419-0710	https://orcid.org/0000-0003-2419-0710	PROPN
iajs-3728	21	4	mailto:basad.a.alsarray@gmail.com	mailto:basad.a.alsarray@gmail.com	X
iajs-3728	22	1	https://orcid.org/0000-0002-0836-5173	https://orcid.org/0000-0002-0836-5173	PROPN
iajs-3728	22	2	mailto:samaa.f.ibraheem@uotechnology.edu.iq	mailto:samaa.f.ibraheem@uotechnology.edu.iq	PROPN
iajs-3728	22	3	https://orcid.org/0000-0003-2419-0710	https://orcid.org/0000-0003-2419-0710	PROPN
iajs-3728	22	4	mailto:basad.a.alsarray@gmail.com	mailto:basad.a.alsarray@gmail.com	X
iajs-3728	22	5	https://orcid.org/0000-0002-0836-5173	https://orcid.org/0000-0002-0836-5173	PROPN
iajs-3728	22	6	mailto:samaa.f.ibraheem@uotechnology.edu.iq	mailto:samaa.f.ibraheem@uotechnology.edu.iq	PROPN
iajs-3728	23	1	https://orcid.org/0000-0003-2419-0710	https://orcid.org/0000-0003-2419-0710	PROPN
iajs-3728	23	2	mailto:basad.a.alsarray@gmail.com	mailto:basad.a.alsarray@gmail.com	X
iajs-3728	23	3	https://orcid.org/0000-0002-0836-5173	https://orcid.org/0000-0002-0836-5173	PROPN
iajs-3728	23	4	mailto:samaa.f.ibraheem@uotechnology.edu.iq	mailto:samaa.f.ibraheem@uotechnology.edu.iq	PROPN
iajs-3728	23	5	https://orcid.org/0000-0003-2419-0710	https://orcid.org/0000-0003-2419-0710	PROPN
iajs-3728	23	6	mailto:basad.a.alsarray@gmail.com	mailto:basad.a.alsarray@gmail.com	X
iajs-3728	23	7	https://orcid.org/0000-0002-0836-5173	https://orcid.org/0000-0002-0836-5173	PROPN
iajs-3728	23	8	mailto:samaa.f.ibraheem@uotechnology.edu.iq	mailto:samaa.f.ibraheem@uotechnology.edu.iq	PROPN
iajs-3728	23	9	https://orcid.org/0000-0003-2419-0710	https://orcid.org/0000-0003-2419-0710	PROPN
iajs-3728	23	10	mailto:basad.a.alsarray@gmail.com	mailto:basad.a.alsarray@gmail.com	X
iajs-3728	23	11	https://orcid.org/0000-0002-0836-5173	https://orcid.org/0000-0002-0836-5173	PROPN
iajs-3728	23	12	mailto:samaa.f.ibraheem@uotechnology.edu.iq	mailto:samaa.f.ibraheem@uotechnology.edu.iq	PROPN
iajs-3728	23	13	https://orcid.org/0000-0003-2419-0710	https://orcid.org/0000-0003-2419-0710	PROPN
iajs-3728	23	14	mailto:basad.a.alsarray@gmail.com	mailto:basad.a.alsarray@gmail.com	PROPN
iajs-3728	23	15	ihjpas	ihjpas	PROPN
iajs-3728	23	16	.	.	PUNCT
iajs-3728	24	1	2025	2025	NUM
iajs-3728	24	2	,	,	PUNCT
iajs-3728	24	3	38(3	38(3	NUM
iajs-3728	24	4	)	)	PUNCT
iajs-3728	24	5	326	326	NUM
iajs-3728	24	6	the	the	DET
iajs-3728	24	7	sbm	sbm	PROPN
iajs-3728	24	8	model	model	NOUN
iajs-3728	24	9	works	work	VERB
iajs-3728	24	10	by	by	ADP
iajs-3728	24	11	randomly	randomly	ADV
iajs-3728	24	12	assigning	assign	VERB
iajs-3728	24	13	nodes	node	NOUN
iajs-3728	24	14	to	to	ADP
iajs-3728	24	15	different	different	ADJ
iajs-3728	24	16	blocks	block	NOUN
iajs-3728	24	17	and	and	CCONJ
iajs-3728	24	18	then	then	ADV
iajs-3728	24	19	connecting	connect	VERB
iajs-3728	24	20	nodes	node	NOUN
iajs-3728	24	21	within	within	ADP
iajs-3728	24	22	each	each	DET
iajs-3728	24	23	block	block	NOUN
iajs-3728	24	24	with	with	ADP
iajs-3728	24	25	a	a	DET
iajs-3728	24	26	certain	certain	ADJ
iajs-3728	24	27	probability	probability	NOUN
iajs-3728	24	28	,	,	PUNCT
iajs-3728	24	29	while	while	SCONJ
iajs-3728	24	30	connecting	connect	VERB
iajs-3728	24	31	nodes	node	NOUN
iajs-3728	24	32	between	between	ADP
iajs-3728	24	33	blocks	block	NOUN
iajs-3728	24	34	with	with	ADP
iajs-3728	24	35	a	a	DET
iajs-3728	24	36	different	different	ADJ
iajs-3728	24	37	probability	probability	NOUN
iajs-3728	24	38	.	.	PUNCT
iajs-3728	25	1	in	in	ADP
iajs-3728	25	2	this	this	DET
iajs-3728	25	3	way	way	NOUN
iajs-3728	25	4	,	,	PUNCT
iajs-3728	25	5	networks	network	NOUN
iajs-3728	25	6	can	can	AUX
iajs-3728	25	7	be	be	AUX
iajs-3728	25	8	created	create	VERB
iajs-3728	25	9	with	with	ADP
iajs-3728	25	10	different	different	ADJ
iajs-3728	25	11	communities	community	NOUN
iajs-3728	25	12	that	that	PRON
iajs-3728	25	13	are	be	AUX
iajs-3728	25	14	connected	connect	VERB
iajs-3728	25	15	to	to	ADP
iajs-3728	25	16	different	different	ADJ
iajs-3728	25	17	degrees	degree	NOUN
iajs-3728	25	18	(	(	PUNCT
iajs-3728	25	19	5	5	NUM
iajs-3728	25	20	,	,	PUNCT
iajs-3728	25	21	6	6	NUM
iajs-3728	25	22	)	)	PUNCT
iajs-3728	25	23	.	.	PUNCT
iajs-3728	26	1	model	model	NOUN
iajs-3728	26	2	selection	selection	NOUN
iajs-3728	26	3	is	be	AUX
iajs-3728	26	4	an	an	DET
iajs-3728	26	5	important	important	ADJ
iajs-3728	26	6	task	task	NOUN
iajs-3728	26	7	in	in	ADP
iajs-3728	26	8	sbm	sbm	PROPN
iajs-3728	26	9	analysis	analysis	NOUN
iajs-3728	26	10	as	as	SCONJ
iajs-3728	26	11	it	it	PRON
iajs-3728	26	12	helps	help	VERB
iajs-3728	26	13	to	to	PART
iajs-3728	26	14	select	select	VERB
iajs-3728	26	15	the	the	DET
iajs-3728	26	16	most	most	ADV
iajs-3728	26	17	accurate	accurate	ADJ
iajs-3728	26	18	model	model	NOUN
iajs-3728	26	19	from	from	ADP
iajs-3728	26	20	a	a	DET
iajs-3728	26	21	set	set	NOUN
iajs-3728	26	22	of	of	ADP
iajs-3728	26	23	candidates	candidate	NOUN
iajs-3728	26	24	(	(	PUNCT
iajs-3728	26	25	7	7	NUM
iajs-3728	26	26	)	)	PUNCT
iajs-3728	26	27	.	.	PUNCT
iajs-3728	27	1	this	this	PRON
iajs-3728	27	2	includes	include	VERB
iajs-3728	27	3	selecting	select	VERB
iajs-3728	27	4	the	the	DET
iajs-3728	27	5	number	number	NOUN
iajs-3728	27	6	of	of	ADP
iajs-3728	27	7	groups	group	NOUN
iajs-3728	27	8	or	or	CCONJ
iajs-3728	27	9	communities	community	NOUN
iajs-3728	27	10	as	as	ADV
iajs-3728	27	11	well	well	ADV
iajs-3728	27	12	as	as	ADP
iajs-3728	27	13	the	the	DET
iajs-3728	27	14	parameters	parameter	NOUN
iajs-3728	27	15	that	that	PRON
iajs-3728	27	16	determine	determine	VERB
iajs-3728	27	17	the	the	DET
iajs-3728	27	18	edge	edge	NOUN
iajs-3728	27	19	probabilities	probability	NOUN
iajs-3728	27	20	within	within	ADP
iajs-3728	27	21	and	and	CCONJ
iajs-3728	27	22	between	between	ADP
iajs-3728	27	23	groups	group	NOUN
iajs-3728	27	24	.	.	PUNCT
iajs-3728	28	1	there	there	PRON
iajs-3728	28	2	are	be	VERB
iajs-3728	28	3	several	several	ADJ
iajs-3728	28	4	approaches	approach	NOUN
iajs-3728	28	5	to	to	ADP
iajs-3728	28	6	model	model	NOUN
iajs-3728	28	7	selection	selection	NOUN
iajs-3728	28	8	for	for	ADP
iajs-3728	28	9	sbm	sbm	NOUN
iajs-3728	28	10	,	,	PUNCT
iajs-3728	28	11	including	include	VERB
iajs-3728	28	12	likelihood	likelihood	NOUN
iajs-3728	28	13	-	-	PUNCT
iajs-3728	28	14	based	base	VERB
iajs-3728	28	15	methods	method	NOUN
iajs-3728	28	16	(	(	PUNCT
iajs-3728	28	17	8	8	NUM
iajs-3728	28	18	,	,	PUNCT
iajs-3728	28	19	9	9	NUM
iajs-3728	28	20	)	)	PUNCT
iajs-3728	28	21	,	,	PUNCT
iajs-3728	28	22	information	information	NOUN
iajs-3728	28	23	-	-	PUNCT
iajs-3728	28	24	theoretic	theoretic	NOUN
iajs-3728	28	25	methods	method	NOUN
iajs-3728	28	26	(	(	PUNCT
iajs-3728	28	27	10	10	NUM
iajs-3728	28	28	)	)	PUNCT
iajs-3728	28	29	,	,	PUNCT
iajs-3728	28	30	spectral	spectral	ADJ
iajs-3728	28	31	clustering	clustering	NOUN
iajs-3728	28	32	(	(	PUNCT
iajs-3728	28	33	11	11	NUM
iajs-3728	28	34	)	)	PUNCT
iajs-3728	28	35	,	,	PUNCT
iajs-3728	28	36	and	and	CCONJ
iajs-3728	28	37	cross	cross	NOUN
iajs-3728	28	38	-	-	ADJ
iajs-3728	28	39	validation	validation	ADJ
iajs-3728	28	40	(	(	PUNCT
iajs-3728	28	41	12	12	NUM
iajs-3728	28	42	)	)	PUNCT
iajs-3728	28	43	.	.	PUNCT
iajs-3728	29	1	likelihood	likelihood	NOUN
iajs-3728	29	2	-	-	PUNCT
iajs-3728	29	3	based	base	VERB
iajs-3728	29	4	methods	method	NOUN
iajs-3728	29	5	are	be	AUX
iajs-3728	29	6	commonly	commonly	ADV
iajs-3728	29	7	used	use	VERB
iajs-3728	29	8	for	for	ADP
iajs-3728	29	9	model	model	NOUN
iajs-3728	29	10	selection	selection	NOUN
iajs-3728	29	11	in	in	ADP
iajs-3728	29	12	sbm	sbm	PROPN
iajs-3728	29	13	analysis	analysis	NOUN
iajs-3728	29	14	.	.	PUNCT
iajs-3728	30	1	here	here	ADV
iajs-3728	30	2	,	,	PUNCT
iajs-3728	30	3	the	the	DET
iajs-3728	30	4	maximum	maximum	ADJ
iajs-3728	30	5	likelihood	likelihood	NOUN
iajs-3728	30	6	estimation	estimation	NOUN
iajs-3728	30	7	(	(	PUNCT
iajs-3728	30	8	mle	mle	NOUN
iajs-3728	30	9	)	)	PUNCT
iajs-3728	30	10	method	method	NOUN
iajs-3728	30	11	is	be	AUX
iajs-3728	30	12	used	use	VERB
iajs-3728	30	13	to	to	PART
iajs-3728	30	14	estimate	estimate	VERB
iajs-3728	30	15	the	the	DET
iajs-3728	30	16	parameters	parameter	NOUN
iajs-3728	30	17	of	of	ADP
iajs-3728	30	18	the	the	DET
iajs-3728	30	19	sbm	sbm	NOUN
iajs-3728	30	20	to	to	PART
iajs-3728	30	21	find	find	VERB
iajs-3728	30	22	the	the	DET
iajs-3728	30	23	parameter	parameter	NOUN
iajs-3728	30	24	values	value	NOUN
iajs-3728	30	25	that	that	PRON
iajs-3728	30	26	maximize	maximize	VERB
iajs-3728	30	27	the	the	DET
iajs-3728	30	28	likelihood	likelihood	NOUN
iajs-3728	30	29	function	function	NOUN
iajs-3728	30	30	.	.	PUNCT
iajs-3728	31	1	mle	mle	PROPN
iajs-3728	31	2	can	can	AUX
iajs-3728	31	3	also	also	ADV
iajs-3728	31	4	be	be	AUX
iajs-3728	31	5	used	use	VERB
iajs-3728	31	6	to	to	PART
iajs-3728	31	7	compare	compare	VERB
iajs-3728	31	8	different	different	ADJ
iajs-3728	31	9	sbms	sbms	NOUN
iajs-3728	31	10	by	by	ADP
iajs-3728	31	11	comparing	compare	VERB
iajs-3728	31	12	their	their	PRON
iajs-3728	31	13	likelihood	likelihood	NOUN
iajs-3728	31	14	values	value	NOUN
iajs-3728	31	15	using	use	VERB
iajs-3728	31	16	different	different	ADJ
iajs-3728	31	17	penalized	penalize	VERB
iajs-3728	31	18	likelihood	likelihood	NOUN
iajs-3728	31	19	information	information	NOUN
iajs-3728	31	20	criteria	criterion	NOUN
iajs-3728	31	21	,	,	PUNCT
iajs-3728	31	22	such	such	ADJ
iajs-3728	31	23	as	as	ADP
iajs-3728	31	24	the	the	DET
iajs-3728	31	25	akaike	akaike	NOUN
iajs-3728	31	26	’s	’s	PART
iajs-3728	31	27	information	information	NOUN
iajs-3728	31	28	criterion	criterion	NOUN
iajs-3728	31	29	(	(	PUNCT
iajs-3728	31	30	aic	aic	PROPN
iajs-3728	31	31	)	)	PUNCT
iajs-3728	31	32	,	,	PUNCT
iajs-3728	31	33	the	the	DET
iajs-3728	31	34	consistent	consistent	ADJ
iajs-3728	31	35	aic	aic	PROPN
iajs-3728	31	36	,	,	PUNCT
iajs-3728	31	37	the	the	DET
iajs-3728	31	38	bayesian	bayesian	ADJ
iajs-3728	31	39	information	information	NOUN
iajs-3728	31	40	criterion	criterion	NOUN
iajs-3728	31	41	(	(	PUNCT
iajs-3728	31	42	bic	bic	PROPN
iajs-3728	31	43	)	)	PUNCT
iajs-3728	31	44	and	and	CCONJ
iajs-3728	31	45	the	the	DET
iajs-3728	31	46	adjusted	adjusted	ADJ
iajs-3728	31	47	bic	bic	NOUN
iajs-3728	31	48	(	(	PUNCT
iajs-3728	31	49	13	13	NUM
iajs-3728	31	50	)	)	PUNCT
iajs-3728	31	51	.	.	PUNCT
iajs-3728	32	1	thus	thus	ADV
iajs-3728	32	2	,	,	PUNCT
iajs-3728	32	3	aic	aic	PROPN
iajs-3728	32	4	is	be	AUX
iajs-3728	32	5	an	an	DET
iajs-3728	32	6	estimate	estimate	NOUN
iajs-3728	32	7	of	of	ADP
iajs-3728	32	8	a	a	DET
iajs-3728	32	9	constant	constant	ADJ
iajs-3728	32	10	plus	plus	CCONJ
iajs-3728	32	11	the	the	DET
iajs-3728	32	12	relative	relative	ADJ
iajs-3728	32	13	distance	distance	NOUN
iajs-3728	32	14	between	between	ADP
iajs-3728	32	15	the	the	DET
iajs-3728	32	16	fitted	fit	VERB
iajs-3728	32	17	likelihood	likelihood	NOUN
iajs-3728	32	18	function	function	NOUN
iajs-3728	32	19	of	of	ADP
iajs-3728	32	20	the	the	DET
iajs-3728	32	21	model	model	NOUN
iajs-3728	32	22	and	and	CCONJ
iajs-3728	32	23	the	the	DET
iajs-3728	32	24	unknown	unknown	ADJ
iajs-3728	32	25	true	true	ADJ
iajs-3728	32	26	likelihood	likelihood	NOUN
iajs-3728	32	27	function	function	NOUN
iajs-3728	32	28	of	of	ADP
iajs-3728	32	29	the	the	DET
iajs-3728	32	30	data	datum	NOUN
iajs-3728	32	31	,	,	PUNCT
iajs-3728	32	32	while	while	SCONJ
iajs-3728	32	33	bic	bic	PROPN
iajs-3728	32	34	is	be	AUX
iajs-3728	32	35	an	an	DET
iajs-3728	32	36	estimate	estimate	NOUN
iajs-3728	32	37	of	of	ADP
iajs-3728	32	38	a	a	DET
iajs-3728	32	39	function	function	NOUN
iajs-3728	32	40	of	of	ADP
iajs-3728	32	41	the	the	DET
iajs-3728	32	42	posterior	posterior	ADJ
iajs-3728	32	43	probability	probability	NOUN
iajs-3728	32	44	of	of	ADP
iajs-3728	32	45	a	a	DET
iajs-3728	32	46	model	model	NOUN
iajs-3728	32	47	being	be	AUX
iajs-3728	32	48	true	true	ADJ
iajs-3728	32	49	under	under	ADP
iajs-3728	32	50	a	a	DET
iajs-3728	32	51	given	give	VERB
iajs-3728	32	52	bayesian	bayesian	NOUN
iajs-3728	32	53	setup	setup	NOUN
iajs-3728	32	54	,	,	PUNCT
iajs-3728	32	55	such	such	ADJ
iajs-3728	32	56	that	that	SCONJ
iajs-3728	32	57	a	a	DET
iajs-3728	32	58	low	low	ADJ
iajs-3728	32	59	aic	aic	PROPN
iajs-3728	32	60	and	and	CCONJ
iajs-3728	32	61	bic	bic	PROPN
iajs-3728	32	62	mean	mean	VERB
iajs-3728	32	63	that	that	SCONJ
iajs-3728	32	64	the	the	DET
iajs-3728	32	65	model	model	NOUN
iajs-3728	32	66	is	be	AUX
iajs-3728	32	67	considered	consider	VERB
iajs-3728	32	68	closer	close	ADV
iajs-3728	32	69	to	to	ADP
iajs-3728	32	70	the	the	DET
iajs-3728	32	71	truth	truth	NOUN
iajs-3728	32	72	(	(	PUNCT
iajs-3728	32	73	14,15	14,15	NUM
iajs-3728	32	74	)	)	PUNCT
iajs-3728	32	75	.	.	PUNCT
iajs-3728	33	1	however	however	ADV
iajs-3728	33	2	,	,	PUNCT
iajs-3728	33	3	optimization	optimization	NOUN
iajs-3728	33	4	of	of	ADP
iajs-3728	33	5	the	the	DET
iajs-3728	33	6	likelihood	likelihood	NOUN
iajs-3728	33	7	function	function	NOUN
iajs-3728	33	8	can	can	AUX
iajs-3728	33	9	be	be	AUX
iajs-3728	33	10	practically	practically	ADV
iajs-3728	33	11	performed	perform	VERB
iajs-3728	33	12	by	by	ADP
iajs-3728	33	13	optimizing	optimize	VERB
iajs-3728	33	14	the	the	DET
iajs-3728	33	15	profile	profile	NOUN
iajs-3728	33	16	likelihood	likelihood	NOUN
iajs-3728	33	17	criteria	criterion	NOUN
iajs-3728	33	18	over	over	ADP
iajs-3728	33	19	all	all	DET
iajs-3728	33	20	possible	possible	ADJ
iajs-3728	33	21	partitions	partition	NOUN
iajs-3728	33	22	for	for	ADP
iajs-3728	33	23	each	each	DET
iajs-3728	33	24	k.	k.	NOUN
iajs-3728	33	25	subsequently	subsequently	ADV
iajs-3728	33	26	,	,	PUNCT
iajs-3728	33	27	the	the	DET
iajs-3728	33	28	best	good	ADJ
iajs-3728	33	29	model	model	NOUN
iajs-3728	33	30	can	can	AUX
iajs-3728	33	31	be	be	AUX
iajs-3728	33	32	selected	select	VERB
iajs-3728	33	33	using	use	VERB
iajs-3728	33	34	aic	aic	PROPN
iajs-3728	33	35	or	or	CCONJ
iajs-3728	33	36	bic	bic	PROPN
iajs-3728	33	37	.	.	PUNCT
iajs-3728	34	1	overall	overall	ADJ
iajs-3728	34	2	,	,	PUNCT
iajs-3728	34	3	likelihood	likelihood	NOUN
iajs-3728	34	4	-	-	PUNCT
iajs-3728	34	5	based	base	VERB
iajs-3728	34	6	methods	method	NOUN
iajs-3728	34	7	provide	provide	VERB
iajs-3728	34	8	a	a	DET
iajs-3728	34	9	rigorous	rigorous	ADJ
iajs-3728	34	10	framework	framework	NOUN
iajs-3728	34	11	for	for	ADP
iajs-3728	34	12	model	model	NOUN
iajs-3728	34	13	selection	selection	NOUN
iajs-3728	34	14	in	in	ADP
iajs-3728	34	15	sbm	sbm	NOUN
iajs-3728	34	16	analysis	analysis	NOUN
iajs-3728	34	17	and	and	CCONJ
iajs-3728	34	18	help	help	VERB
iajs-3728	34	19	researchers	researcher	NOUN
iajs-3728	34	20	to	to	PART
iajs-3728	34	21	select	select	VERB
iajs-3728	34	22	the	the	DET
iajs-3728	34	23	best	well	ADV
iajs-3728	34	24	-	-	PUNCT
iajs-3728	34	25	fitting	fit	VERB
iajs-3728	34	26	model	model	NOUN
iajs-3728	34	27	based	base	VERB
iajs-3728	34	28	on	on	ADP
iajs-3728	34	29	statistical	statistical	ADJ
iajs-3728	34	30	criteria	criterion	NOUN
iajs-3728	34	31	.	.	PUNCT
iajs-3728	35	1	moreover	moreover	ADV
iajs-3728	35	2	,	,	PUNCT
iajs-3728	35	3	model	model	NOUN
iajs-3728	35	4	selection	selection	NOUN
iajs-3728	35	5	for	for	ADP
iajs-3728	35	6	sbm	sbm	PROPN
iajs-3728	35	7	can	can	AUX
iajs-3728	35	8	be	be	AUX
iajs-3728	35	9	challenging	challenge	VERB
iajs-3728	35	10	due	due	ADJ
iajs-3728	35	11	to	to	ADP
iajs-3728	35	12	the	the	DET
iajs-3728	35	13	high	high	ADV
iajs-3728	35	14	-	-	PUNCT
iajs-3728	35	15	dimensional	dimensional	ADJ
iajs-3728	35	16	parameter	parameter	NOUN
iajs-3728	35	17	space	space	NOUN
iajs-3728	35	18	and	and	CCONJ
iajs-3728	35	19	non	non	ADJ
iajs-3728	35	20	-	-	ADJ
iajs-3728	35	21	convex	convex	ADJ
iajs-3728	35	22	optimization	optimization	NOUN
iajs-3728	35	23	problem	problem	NOUN
iajs-3728	35	24	(	(	PUNCT
iajs-3728	35	25	16	16	NUM
iajs-3728	35	26	)	)	PUNCT
iajs-3728	35	27	.	.	PUNCT
iajs-3728	36	1	therefore	therefore	ADV
iajs-3728	36	2	,	,	PUNCT
iajs-3728	36	3	it	it	PRON
iajs-3728	36	4	is	be	AUX
iajs-3728	36	5	important	important	ADJ
iajs-3728	36	6	to	to	PART
iajs-3728	36	7	use	use	VERB
iajs-3728	36	8	multiple	multiple	ADJ
iajs-3728	36	9	approaches	approach	NOUN
iajs-3728	36	10	and	and	CCONJ
iajs-3728	36	11	carefully	carefully	ADV
iajs-3728	36	12	evaluate	evaluate	VERB
iajs-3728	36	13	their	their	PRON
iajs-3728	36	14	results	result	NOUN
iajs-3728	36	15	before	before	ADP
iajs-3728	36	16	drawing	draw	VERB
iajs-3728	36	17	conclusions	conclusion	NOUN
iajs-3728	36	18	about	about	ADP
iajs-3728	36	19	the	the	DET
iajs-3728	36	20	underlying	underlie	VERB
iajs-3728	36	21	structure	structure	NOUN
iajs-3728	36	22	of	of	ADP
iajs-3728	36	23	the	the	DET
iajs-3728	36	24	network	network	NOUN
iajs-3728	36	25	.	.	PUNCT
iajs-3728	37	1	the	the	DET
iajs-3728	37	2	stochastic	stochastic	ADJ
iajs-3728	37	3	block	block	NOUN
iajs-3728	37	4	models	model	NOUN
iajs-3728	37	5	(	(	PUNCT
iajs-3728	37	6	sbms	sbms	ADJ
iajs-3728	37	7	)	)	PUNCT
iajs-3728	37	8	are	be	AUX
iajs-3728	37	9	popular	popular	ADJ
iajs-3728	37	10	probabilistic	probabilistic	ADJ
iajs-3728	37	11	models	model	NOUN
iajs-3728	37	12	for	for	ADP
iajs-3728	37	13	community	community	NOUN
iajs-3728	37	14	detection	detection	NOUN
iajs-3728	37	15	in	in	ADP
iajs-3728	37	16	networks	network	NOUN
iajs-3728	37	17	.	.	PUNCT
iajs-3728	38	1	they	they	PRON
iajs-3728	38	2	provide	provide	VERB
iajs-3728	38	3	a	a	DET
iajs-3728	38	4	powerful	powerful	ADJ
iajs-3728	38	5	and	and	CCONJ
iajs-3728	38	6	modern	modern	ADJ
iajs-3728	38	7	way	way	NOUN
iajs-3728	38	8	to	to	PART
iajs-3728	38	9	define	define	VERB
iajs-3728	38	10	and	and	CCONJ
iajs-3728	38	11	understand	understand	VERB
iajs-3728	38	12	network	network	NOUN
iajs-3728	38	13	structure	structure	NOUN
iajs-3728	38	14	(	(	PUNCT
iajs-3728	38	15	17	17	NUM
iajs-3728	38	16	)	)	PUNCT
iajs-3728	38	17	.	.	PUNCT
iajs-3728	39	1	there	there	PRON
iajs-3728	39	2	are	be	VERB
iajs-3728	39	3	many	many	ADJ
iajs-3728	39	4	types	type	NOUN
iajs-3728	39	5	of	of	ADP
iajs-3728	39	6	sbms	sbms	NOUN
iajs-3728	39	7	,	,	PUNCT
iajs-3728	39	8	including	include	VERB
iajs-3728	39	9	symmetric	symmetric	ADJ
iajs-3728	39	10	sbms	sbms	ADJ
iajs-3728	39	11	and	and	CCONJ
iajs-3728	39	12	overlapping	overlap	VERB
iajs-3728	39	13	sbms	sbms	NOUN
iajs-3728	39	14	(	(	PUNCT
iajs-3728	39	15	18	18	NUM
iajs-3728	39	16	)	)	PUNCT
iajs-3728	39	17	,	,	PUNCT
iajs-3728	39	18	but	but	CCONJ
iajs-3728	39	19	the	the	DET
iajs-3728	39	20	focus	focus	NOUN
iajs-3728	39	21	is	be	AUX
iajs-3728	39	22	on	on	ADP
iajs-3728	39	23	sbms	sbms	NOUN
iajs-3728	39	24	in	in	ADP
iajs-3728	39	25	standard	standard	ADJ
iajs-3728	39	26	form	form	NOUN
iajs-3728	39	27	and	and	CCONJ
iajs-3728	39	28	sbms	sbms	VERB
iajs-3728	39	29	with	with	ADP
iajs-3728	39	30	corrected	correct	VERB
iajs-3728	39	31	degree	degree	NOUN
iajs-3728	39	32	.	.	PUNCT
iajs-3728	40	1	to	to	PART
iajs-3728	40	2	represent	represent	VERB
iajs-3728	40	3	the	the	DET
iajs-3728	40	4	standard	standard	ADJ
iajs-3728	40	5	sbm	sbm	NOUN
iajs-3728	40	6	,	,	PUNCT
iajs-3728	40	7	an	an	DET
iajs-3728	40	8	undirected	undirected	ADJ
iajs-3728	40	9	and	and	CCONJ
iajs-3728	40	10	unweighted	unweighted	ADJ
iajs-3728	40	11	random	random	ADJ
iajs-3728	40	12	graph	graph	NOUN
iajs-3728	40	13	g	g	NOUN
iajs-3728	40	14	with	with	ADP
iajs-3728	40	15	n	n	NOUN
iajs-3728	40	16	nodes	node	NOUN
iajs-3728	40	17	is	be	AUX
iajs-3728	40	18	considered	consider	VERB
iajs-3728	40	19	,	,	PUNCT
iajs-3728	40	20	where	where	SCONJ
iajs-3728	40	21	a	a	DET
iajs-3728	40	22	=	=	SYM
iajs-3728	40	23	[	[	X
iajs-3728	40	24	aij]∈r	aij]∈r	NOUN
iajs-3728	40	25	n×n	n×n	PROPN
iajs-3728	40	26	is	be	AUX
iajs-3728	40	27	the	the	DET
iajs-3728	40	28	adjacency	adjacency	NOUN
iajs-3728	40	29	matrix	matrix	NOUN
iajs-3728	40	30	of	of	ADP
iajs-3728	40	31	g	g	PROPN
iajs-3728	40	32	that	that	PRON
iajs-3728	40	33	characterizes	characterize	VERB
iajs-3728	40	34	the	the	DET
iajs-3728	40	35	relation	relation	NOUN
iajs-3728	40	36	between	between	ADP
iajs-3728	40	37	each	each	DET
iajs-3728	40	38	pair	pair	NOUN
iajs-3728	40	39	of	of	ADP
iajs-3728	40	40	vertices	vertex	NOUN
iajs-3728	40	41	i	i	PRON
iajs-3728	40	42	and	and	CCONJ
iajs-3728	40	43	j.	j.	PROPN
iajs-3728	40	44	a	a	PROPN
iajs-3728	40	45	is	be	AUX
iajs-3728	40	46	symmetric	symmetric	ADJ
iajs-3728	40	47	.	.	PUNCT
iajs-3728	41	1	to	to	PART
iajs-3728	41	2	model	model	VERB
iajs-3728	41	3	a	a	PRON
iajs-3728	41	4	by	by	ADP
iajs-3728	41	5	sbm	sbm	PROPN
iajs-3728	41	6	,	,	PUNCT
iajs-3728	41	7	assume	assume	VERB
iajs-3728	41	8	that	that	SCONJ
iajs-3728	41	9	aij	aij	PROPN
iajs-3728	41	10	s	s	PROPN
iajs-3728	41	11	are	be	AUX
iajs-3728	41	12	independent	independent	ADJ
iajs-3728	41	13	bernoulli	bernoulli	NOUN
iajs-3728	41	14	variables	variable	NOUN
iajs-3728	41	15	,	,	PUNCT
iajs-3728	41	16	that	that	ADV
iajs-3728	41	17	is	is	ADV
iajs-3728	41	18	(	(	PUNCT
iajs-3728	41	19	)	)	PUNCT
iajs-3728	41	20	∈	∈	PROPN
iajs-3728	41	21	(	(	PUNCT
iajs-3728	41	22	)	)	PUNCT
iajs-3728	41	23	(	(	PUNCT
iajs-3728	41	24	1	1	X
iajs-3728	41	25	)	)	PUNCT
iajs-3728	41	26	and	and	CCONJ
iajs-3728	41	27	the	the	DET
iajs-3728	41	28	vertices	vertex	NOUN
iajs-3728	41	29	in	in	ADP
iajs-3728	41	30	g	g	PROPN
iajs-3728	41	31	are	be	AUX
iajs-3728	41	32	partitioned	partition	VERB
iajs-3728	41	33	into	into	ADP
iajs-3728	41	34	k	k	PROPN
iajs-3728	41	35	groups	group	NOUN
iajs-3728	41	36	(	(	PUNCT
iajs-3728	41	37	communities	community	NOUN
iajs-3728	41	38	)	)	PUNCT
iajs-3728	41	39	such	such	ADJ
iajs-3728	41	40	that	that	SCONJ
iajs-3728	41	41	the	the	DET
iajs-3728	41	42	vertex	vertex	NOUN
iajs-3728	41	43	i	i	PRON
iajs-3728	41	44	belongs	belong	VERB
iajs-3728	41	45	to	to	ADP
iajs-3728	41	46	the	the	DET
iajs-3728	41	47	community	community	NOUN
iajs-3728	41	48	,	,	PUNCT
iajs-3728	41	49	…	…	PUNCT
iajs-3728	41	50	,	,	PUNCT
iajs-3728	41	51	k	k	NOUN
iajs-3728	41	52	,	,	PUNCT
iajs-3728	41	53	according	accord	VERB
iajs-3728	41	54	to	to	ADP
iajs-3728	41	55	the	the	DET
iajs-3728	41	56	map	map	NOUN
iajs-3728	41	57	c	c	NOUN
iajs-3728	41	58	:	:	PUNCT
iajs-3728	41	59	{	{	PUNCT
iajs-3728	41	60	1	1	NUM
iajs-3728	41	61	,	,	PUNCT
iajs-3728	41	62	…	…	PUNCT
iajs-3728	41	63	,	,	PUNCT
iajs-3728	41	64	n	n	CCONJ
iajs-3728	41	65	}	}	PUNCT
iajs-3728	41	66	→	→	SYM
iajs-3728	41	67	{	{	PUNCT
iajs-3728	41	68	1	1	NUM
iajs-3728	41	69	,	,	PUNCT
iajs-3728	41	70	…	…	PUNCT
iajs-3728	41	71	,	,	PUNCT
iajs-3728	41	72	k	k	NOUN
iajs-3728	41	73	}	}	PUNCT
iajs-3728	41	74	such	such	ADJ
iajs-3728	41	75	that	that	PRON
iajs-3728	41	76	.	.	PUNCT
iajs-3728	42	1	let	let	VERB
iajs-3728	42	2	z	z	NOUN
iajs-3728	42	3	∈	∈	PROPN
iajs-3728	42	4	{	{	PUNCT
iajs-3728	42	5	1	1	NUM
iajs-3728	42	6	,	,	PUNCT
iajs-3728	42	7	…	…	PUNCT
iajs-3728	42	8	,	,	PUNCT
iajs-3728	42	9	k	k	NOUN
iajs-3728	42	10	}	}	PUNCT
iajs-3728	42	11	n	n	PRON
iajs-3728	42	12	be	be	VERB
iajs-3728	42	13	the	the	DET
iajs-3728	42	14	vector	vector	NOUN
iajs-3728	42	15	of	of	ADP
iajs-3728	42	16	labels	label	NOUN
iajs-3728	42	17	that	that	PRON
iajs-3728	42	18	members	member	VERB
iajs-3728	42	19	the	the	DET
iajs-3728	42	20	vertices	vertex	NOUN
iajs-3728	42	21	i′s	i′s	VERB
iajs-3728	42	22	to	to	ADP
iajs-3728	42	23	one	one	NUM
iajs-3728	42	24	of	of	ADP
iajs-3728	42	25	k	k	PROPN
iajs-3728	42	26	communities	community	NOUN
iajs-3728	42	27	,	,	PUNCT
iajs-3728	42	28	i.e.	i.e.	X
iajs-3728	42	29	,	,	PUNCT
iajs-3728	42	30	*	*	PUNCT
iajs-3728	43	1	+	+	ADJ
iajs-3728	43	2	,	,	PUNCT
iajs-3728	43	3	where	where	SCONJ
iajs-3728	43	4	(	(	PUNCT
iajs-3728	43	5	)	)	PUNCT
iajs-3728	43	6	the	the	DET
iajs-3728	43	7	membership	membership	NOUN
iajs-3728	43	8	label	label	NOUN
iajs-3728	43	9	z	z	PROPN
iajs-3728	43	10	that	that	PRON
iajs-3728	43	11	defines	define	VERB
iajs-3728	43	12	community	community	NOUN
iajs-3728	43	13	assignment	assignment	NOUN
iajs-3728	43	14	is	be	AUX
iajs-3728	43	15	drawn	draw	VERB
iajs-3728	43	16	independently	independently	ADV
iajs-3728	43	17	from	from	ADP
iajs-3728	43	18	a	a	DET
iajs-3728	43	19	multinomial	multinomial	ADJ
iajs-3728	43	20	distribution	distribution	NOUN
iajs-3728	43	21	on	on	ADP
iajs-3728	43	22	k	k	PROPN
iajs-3728	43	23	communities	community	NOUN
iajs-3728	43	24	with	with	ADP
iajs-3728	43	25	probability	probability	NOUN
iajs-3728	43	26	(	(	PUNCT
iajs-3728	43	27	)	)	PUNCT
iajs-3728	43	28	∑	∑	ADV
iajs-3728	43	29	,	,	PUNCT
iajs-3728	43	30	that	that	PRON
iajs-3728	43	31	is	is	AUX
iajs-3728	43	32	i	i	PRON
iajs-3728	43	33	i	i	NOUN
iajs-3728	43	34	d	d	NOUN
iajs-3728	43	35	multinomial	multinomial	ADJ
iajs-3728	43	36	(	(	PUNCT
iajs-3728	43	37	)	)	PUNCT
iajs-3728	43	38	(	(	PUNCT
iajs-3728	43	39	2	2	X
iajs-3728	43	40	)	)	PUNCT
iajs-3728	43	41	in	in	ADP
iajs-3728	43	42	sbm	sbm	PROPN
iajs-3728	43	43	,	,	PUNCT
iajs-3728	43	44	the	the	DET
iajs-3728	43	45	probability	probability	NOUN
iajs-3728	43	46	that	that	PRON
iajs-3728	43	47	vertices	vertice	VERB
iajs-3728	43	48	i	i	PRON
iajs-3728	43	49	and	and	CCONJ
iajs-3728	43	50	j	j	PROPN
iajs-3728	43	51	are	be	AUX
iajs-3728	43	52	connected	connect	VERB
iajs-3728	43	53	(	(	PUNCT
iajs-3728	43	54	(	(	PUNCT
iajs-3728	43	55	)	)	PUNCT
iajs-3728	43	56	)	)	PUNCT
iajs-3728	43	57	depends	depend	VERB
iajs-3728	43	58	on	on	ADP
iajs-3728	43	59	their	their	PRON
iajs-3728	43	60	community	community	NOUN
iajs-3728	43	61	membership	membership	NOUN
iajs-3728	43	62	;	;	PUNCT
iajs-3728	43	63	therefor	therefor	PROPN
iajs-3728	43	64	,	,	PUNCT
iajs-3728	43	65	is	be	AUX
iajs-3728	43	66	identical	identical	ADJ
iajs-3728	43	67	for	for	ADP
iajs-3728	43	68	any	any	DET
iajs-3728	43	69	i	i	PRON
iajs-3728	43	70	and	and	CCONJ
iajs-3728	43	71	j	j	PROPN
iajs-3728	43	72	that	that	PRON
iajs-3728	43	73	belong	belong	VERB
iajs-3728	43	74	to	to	ADP
iajs-3728	43	75	the	the	DET
iajs-3728	43	76	same	same	ADJ
iajs-3728	43	77	ihjpas	ihjpa	NOUN
iajs-3728	43	78	.	.	PUNCT
iajs-3728	44	1	2025	2025	NUM
iajs-3728	44	2	,	,	PUNCT
iajs-3728	44	3	38(3	38(3	NUM
iajs-3728	44	4	)	)	PUNCT
iajs-3728	44	5	327	327	NUM
iajs-3728	44	6	community	community	NOUN
iajs-3728	44	7	.	.	PUNCT
iajs-3728	45	1	by	by	ADP
iajs-3728	45	2	this	this	DET
iajs-3728	45	3	way	way	NOUN
iajs-3728	45	4	,	,	PUNCT
iajs-3728	45	5	a	a	DET
iajs-3728	45	6	symmetric	symmetric	ADJ
iajs-3728	45	7	k	k	PROPN
iajs-3728	45	8	×	×	PROPN
iajs-3728	45	9	k	k	PROPN
iajs-3728	45	10	,	,	PUNCT
iajs-3728	45	11	matrix	matrix	NOUN
iajs-3728	45	12	b	b	NOUN
iajs-3728	45	13	is	be	AUX
iajs-3728	45	14	formed	form	VERB
iajs-3728	45	15	such	such	ADJ
iajs-3728	45	16	that	that	SCONJ
iajs-3728	45	17	where	where	SCONJ
iajs-3728	45	18	∈	∈	PROPN
iajs-3728	45	19	,	,	PUNCT
iajs-3728	45	20	and	and	CCONJ
iajs-3728	45	21	k	k	PROPN
iajs-3728	45	22	≪	≪	PUNCT
iajs-3728	45	23	n.	n.	NOUN
iajs-3728	45	24	this	this	DET
iajs-3728	45	25	probability	probability	NOUN
iajs-3728	45	26	matrix	matrix	NOUN
iajs-3728	45	27	is	be	AUX
iajs-3728	45	28	called	call	VERB
iajs-3728	45	29	a	a	DET
iajs-3728	45	30	stochastic	stochastic	ADJ
iajs-3728	45	31	block	block	NOUN
iajs-3728	45	32	matrix	matrix	NOUN
iajs-3728	45	33	or	or	CCONJ
iajs-3728	45	34	connectivity	connectivity	NOUN
iajs-3728	45	35	matrix	matrix	NOUN
iajs-3728	45	36	,	,	PUNCT
iajs-3728	45	37	so	so	SCONJ
iajs-3728	45	38	there	there	PRON
iajs-3728	45	39	are	be	VERB
iajs-3728	45	40	k	k	X
iajs-3728	45	41	(	(	PUNCT
iajs-3728	45	42	k	k	PROPN
iajs-3728	45	43	+	+	PROPN
iajs-3728	45	44	1)/2	1)/2	NUM
iajs-3728	45	45	connectivity	connectivity	NOUN
iajs-3728	45	46	parameters	parameter	NOUN
iajs-3728	45	47	to	to	PART
iajs-3728	45	48	estimate	estimate	VERB
iajs-3728	45	49	in	in	ADP
iajs-3728	45	50	this	this	DET
iajs-3728	45	51	model	model	NOUN
iajs-3728	45	52	(	(	PUNCT
iajs-3728	45	53	19	19	NUM
iajs-3728	45	54	)	)	PUNCT
iajs-3728	45	55	.	.	PUNCT
iajs-3728	46	1	in	in	ADP
iajs-3728	46	2	networks	network	NOUN
iajs-3728	46	3	with	with	ADP
iajs-3728	46	4	community	community	NOUN
iajs-3728	46	5	structures	structure	NOUN
iajs-3728	46	6	,	,	PUNCT
iajs-3728	46	7	the	the	DET
iajs-3728	46	8	edge	edge	NOUN
iajs-3728	46	9	probabilities	probability	NOUN
iajs-3728	46	10	within	within	ADP
iajs-3728	46	11	a	a	DET
iajs-3728	46	12	same	same	ADJ
iajs-3728	46	13	block	block	NOUN
iajs-3728	46	14	exceed	exceed	VERB
iajs-3728	46	15	the	the	DET
iajs-3728	46	16	probabilities	probability	NOUN
iajs-3728	46	17	that	that	PRON
iajs-3728	46	18	vertices	vertice	VERB
iajs-3728	46	19	of	of	ADP
iajs-3728	46	20	different	different	ADJ
iajs-3728	46	21	blocks	block	NOUN
iajs-3728	46	22	are	be	AUX
iajs-3728	46	23	connected	connect	VERB
iajs-3728	46	24	,	,	PUNCT
iajs-3728	46	25	i.e.	i.e.	X
iajs-3728	46	26	,	,	PUNCT
iajs-3728	46	27	the	the	DET
iajs-3728	46	28	vertices	vertex	NOUN
iajs-3728	46	29	within	within	ADP
iajs-3728	46	30	a	a	DET
iajs-3728	46	31	same	same	ADJ
iajs-3728	46	32	block	block	NOUN
iajs-3728	46	33	are	be	AUX
iajs-3728	46	34	more	more	ADV
iajs-3728	46	35	likely	likely	ADJ
iajs-3728	46	36	to	to	PART
iajs-3728	46	37	be	be	AUX
iajs-3728	46	38	connected	connect	VERB
iajs-3728	46	39	,	,	PUNCT
iajs-3728	46	40	so	so	CCONJ
iajs-3728	46	41	in	in	ADP
iajs-3728	46	42	community	community	NOUN
iajs-3728	46	43	detection	detection	NOUN
iajs-3728	46	44	,	,	PUNCT
iajs-3728	46	45	we	we	PRON
iajs-3728	46	46	seek	seek	VERB
iajs-3728	46	47	for	for	ADP
iajs-3728	46	48	,	,	PUNCT
iajs-3728	46	49	∀k	∀k	NOUN
iajs-3728	46	50	,	,	PUNCT
iajs-3728	46	51	q	q	X
iajs-3728	46	52	,	,	PUNCT
iajs-3728	46	53	r=1	r=1	NOUN
iajs-3728	46	54	,	,	PUNCT
iajs-3728	46	55	2	2	NUM
iajs-3728	46	56	,	,	PUNCT
iajs-3728	46	57	·	·	PUNCT
iajs-3728	46	58	·	·	PUNCT
iajs-3728	47	1	·	·	PUNCT
iajs-3728	47	2	,	,	PUNCT
iajs-3728	47	3	k	k	X
iajs-3728	47	4	,	,	PUNCT
iajs-3728	47	5	with	with	ADP
iajs-3728	47	6	qr	qr	PROPN
iajs-3728	47	7	.	.	PUNCT
iajs-3728	48	1	the	the	DET
iajs-3728	48	2	degree	degree	NOUN
iajs-3728	48	3	-	-	PUNCT
iajs-3728	48	4	corrected	correct	VERB
iajs-3728	48	5	stochastic	stochastic	ADJ
iajs-3728	48	6	block	block	NOUN
iajs-3728	48	7	model	model	NOUN
iajs-3728	48	8	is	be	AUX
iajs-3728	48	9	a	a	DET
iajs-3728	48	10	probabilistic	probabilistic	ADJ
iajs-3728	48	11	model	model	NOUN
iajs-3728	48	12	for	for	ADP
iajs-3728	48	13	network	network	NOUN
iajs-3728	48	14	data	datum	NOUN
iajs-3728	48	15	that	that	PRON
iajs-3728	48	16	extends	extend	VERB
iajs-3728	48	17	the	the	DET
iajs-3728	48	18	traditional	traditional	ADJ
iajs-3728	48	19	stochastic	stochastic	ADJ
iajs-3728	48	20	block	block	NOUN
iajs-3728	48	21	model	model	NOUN
iajs-3728	48	22	(	(	PUNCT
iajs-3728	48	23	sbm	sbm	PROPN
iajs-3728	48	24	)	)	PUNCT
iajs-3728	48	25	by	by	ADP
iajs-3728	48	26	incorporating	incorporate	VERB
iajs-3728	48	27	node	node	NOUN
iajs-3728	48	28	-	-	PUNCT
iajs-3728	48	29	specific	specific	ADJ
iajs-3728	48	30	degree	degree	NOUN
iajs-3728	48	31	information	information	NOUN
iajs-3728	48	32	.	.	PUNCT
iajs-3728	49	1	in	in	ADP
iajs-3728	49	2	the	the	DET
iajs-3728	49	3	degree	degree	NOUN
iajs-3728	49	4	corrected	correct	VERB
iajs-3728	49	5	sbm	sbm	PROPN
iajs-3728	49	6	,	,	PUNCT
iajs-3728	49	7	each	each	DET
iajs-3728	49	8	node	node	NOUN
iajs-3728	49	9	is	be	AUX
iajs-3728	49	10	assigned	assign	VERB
iajs-3728	49	11	to	to	ADP
iajs-3728	49	12	one	one	NUM
iajs-3728	49	13	of	of	ADP
iajs-3728	49	14	k	k	PROPN
iajs-3728	49	15	blocks	block	NOUN
iajs-3728	49	16	,	,	PUNCT
iajs-3728	49	17	and	and	CCONJ
iajs-3728	49	18	the	the	DET
iajs-3728	49	19	probability	probability	NOUN
iajs-3728	49	20	of	of	ADP
iajs-3728	49	21	an	an	DET
iajs-3728	49	22	edge	edge	NOUN
iajs-3728	49	23	between	between	ADP
iajs-3728	49	24	two	two	NUM
iajs-3728	49	25	nodes	node	NOUN
iajs-3728	49	26	depends	depend	VERB
iajs-3728	49	27	on	on	ADP
iajs-3728	49	28	their	their	PRON
iajs-3728	49	29	block	block	NOUN
iajs-3728	49	30	assignments	assignment	NOUN
iajs-3728	49	31	as	as	ADV
iajs-3728	49	32	well	well	ADV
iajs-3728	49	33	as	as	ADP
iajs-3728	49	34	their	their	PRON
iajs-3728	49	35	individual	individual	ADJ
iajs-3728	49	36	degrees	degree	NOUN
iajs-3728	49	37	.	.	PUNCT
iajs-3728	50	1	so	so	ADV
iajs-3728	50	2	the	the	DET
iajs-3728	50	3	probability	probability	NOUN
iajs-3728	50	4	matrix	matrix	NOUN
iajs-3728	50	5	b	b	NOUN
iajs-3728	50	6	,	,	PUNCT
iajs-3728	50	7	with	with	ADP
iajs-3728	50	8	size	size	NOUN
iajs-3728	50	9	k	k	PROPN
iajs-3728	50	10	×	×	PROPN
iajs-3728	50	11	k	k	PROPN
iajs-3728	50	12	,	,	PUNCT
iajs-3728	50	13	is	be	AUX
iajs-3728	50	14	formed	form	VERB
iajs-3728	50	15	as	as	ADP
iajs-3728	50	16	where	where	SCONJ
iajs-3728	50	17	θi>0	θi>0	PROPN
iajs-3728	50	18	is	be	AUX
iajs-3728	50	19	an	an	DET
iajs-3728	50	20	additional	additional	ADJ
iajs-3728	50	21	parameter	parameter	NOUN
iajs-3728	50	22	assigned	assign	VERB
iajs-3728	50	23	to	to	ADP
iajs-3728	50	24	each	each	DET
iajs-3728	50	25	node	node	NOUN
iajs-3728	50	26	to	to	PART
iajs-3728	50	27	control	control	VERB
iajs-3728	50	28	its	its	PRON
iajs-3728	50	29	expected	expect	VERB
iajs-3728	50	30	degree	degree	NOUN
iajs-3728	50	31	(	(	PUNCT
iajs-3728	50	32	1	1	NUM
iajs-3728	50	33	)	)	PUNCT
iajs-3728	50	34	.	.	PUNCT
iajs-3728	51	1	moreover	moreover	ADV
iajs-3728	51	2	,	,	PUNCT
iajs-3728	51	3	the	the	DET
iajs-3728	51	4	value	value	NOUN
iajs-3728	51	5	aij	aij	PROPN
iajs-3728	51	6	is	be	AUX
iajs-3728	51	7	a	a	DET
iajs-3728	51	8	poisson	poisson	NOUN
iajs-3728	51	9	-	-	PUNCT
iajs-3728	51	10	distributed	distribute	VERB
iajs-3728	51	11	random	random	ADJ
iajs-3728	51	12	variable	variable	NOUN
iajs-3728	51	13	with	with	ADP
iajs-3728	51	14	mean	mean	ADJ
iajs-3728	51	15	,	,	PUNCT
iajs-3728	51	16	i.e.	i.e.	X
iajs-3728	51	17	,	,	PUNCT
iajs-3728	51	18	(	(	PUNCT
iajs-3728	51	19	)	)	PUNCT
iajs-3728	51	20	(	(	PUNCT
iajs-3728	51	21	3	3	X
iajs-3728	51	22	)	)	PUNCT
iajs-3728	51	23	in	in	ADP
iajs-3728	51	24	this	this	DET
iajs-3728	51	25	paper	paper	NOUN
iajs-3728	51	26	,	,	PUNCT
iajs-3728	51	27	a	a	DET
iajs-3728	51	28	model	model	NOUN
iajs-3728	51	29	selection	selection	NOUN
iajs-3728	51	30	for	for	ADP
iajs-3728	51	31	stochastic	stochastic	ADJ
iajs-3728	51	32	block	block	NOUN
iajs-3728	51	33	models	model	NOUN
iajs-3728	51	34	is	be	AUX
iajs-3728	51	35	performed	perform	VERB
iajs-3728	51	36	based	base	VERB
iajs-3728	51	37	on	on	ADP
iajs-3728	51	38	optimizing	optimize	VERB
iajs-3728	51	39	the	the	DET
iajs-3728	51	40	log	log	NOUN
iajs-3728	51	41	likelihood	likelihood	NOUN
iajs-3728	51	42	function	function	NOUN
iajs-3728	51	43	to	to	PART
iajs-3728	51	44	find	find	VERB
iajs-3728	51	45	the	the	DET
iajs-3728	51	46	best	good	ADJ
iajs-3728	51	47	number	number	NOUN
iajs-3728	51	48	k	k	PROPN
iajs-3728	51	49	of	of	ADP
iajs-3728	51	50	communities	community	NOUN
iajs-3728	51	51	.	.	PUNCT
iajs-3728	52	1	the	the	DET
iajs-3728	52	2	main	main	ADJ
iajs-3728	52	3	aim	aim	NOUN
iajs-3728	52	4	is	be	AUX
iajs-3728	52	5	to	to	PART
iajs-3728	52	6	select	select	VERB
iajs-3728	52	7	the	the	DET
iajs-3728	52	8	model	model	NOUN
iajs-3728	52	9	for	for	ADP
iajs-3728	52	10	communities	community	NOUN
iajs-3728	52	11	that	that	PRON
iajs-3728	52	12	are	be	AUX
iajs-3728	52	13	detected	detect	VERB
iajs-3728	52	14	using	use	VERB
iajs-3728	52	15	a	a	DET
iajs-3728	52	16	convex	convex	ADJ
iajs-3728	52	17	optimization	optimization	NOUN
iajs-3728	52	18	approach	approach	NOUN
iajs-3728	52	19	under	under	ADP
iajs-3728	52	20	degree	degree	NOUN
iajs-3728	52	21	corrected	correct	VERB
iajs-3728	52	22	sbm	sbm	PROPN
iajs-3728	52	23	,	,	PUNCT
iajs-3728	52	24	which	which	PRON
iajs-3728	52	25	is	be	AUX
iajs-3728	52	26	regularized	regularize	VERB
iajs-3728	52	27	convex	convex	ADJ
iajs-3728	52	28	modularity	modularity	NOUN
iajs-3728	52	29	maximization	maximization	NOUN
iajs-3728	52	30	method	method	NOUN
iajs-3728	52	31	(	(	PUNCT
iajs-3728	52	32	rcmm	rcmm	NOUN
iajs-3728	52	33	)	)	PUNCT
iajs-3728	52	34	that	that	PRON
iajs-3728	52	35	is	be	AUX
iajs-3728	52	36	suggested	suggest	VERB
iajs-3728	52	37	in	in	ADP
iajs-3728	52	38	(	(	PUNCT
iajs-3728	52	39	20	20	NUM
iajs-3728	52	40	)	)	PUNCT
iajs-3728	52	41	,	,	PUNCT
iajs-3728	52	42	and	and	CCONJ
iajs-3728	52	43	compare	compare	VERB
iajs-3728	52	44	this	this	DET
iajs-3728	52	45	result	result	NOUN
iajs-3728	52	46	with	with	ADP
iajs-3728	52	47	others	other	NOUN
iajs-3728	52	48	detected	detect	VERB
iajs-3728	52	49	using	use	VERB
iajs-3728	52	50	various	various	ADJ
iajs-3728	52	51	algorithms	algorithm	NOUN
iajs-3728	52	52	,	,	PUNCT
iajs-3728	52	53	which	which	PRON
iajs-3728	52	54	are	be	AUX
iajs-3728	52	55	convex	convex	ADJ
iajs-3728	52	56	modularity	modularity	NOUN
iajs-3728	52	57	maximization	maximization	PROPN
iajs-3728	52	58	cmm	cmm	PROPN
iajs-3728	52	59	(	(	PUNCT
iajs-3728	52	60	4	4	NUM
iajs-3728	52	61	,	,	PUNCT
iajs-3728	52	62	21	21	NUM
iajs-3728	52	63	)	)	PUNCT
iajs-3728	52	64	,	,	PUNCT
iajs-3728	52	65	modularity	modularity	NOUN
iajs-3728	52	66	maximization	maximization	NOUN
iajs-3728	52	67	algorithm	algorithm	NOUN
iajs-3728	52	68	(	(	PUNCT
iajs-3728	52	69	22	22	NUM
iajs-3728	52	70	)	)	PUNCT
iajs-3728	52	71	,	,	PUNCT
iajs-3728	52	72	danon	danon	ADJ
iajs-3728	52	73	algorithm	algorithm	NOUN
iajs-3728	52	74	(	(	PUNCT
iajs-3728	52	75	23	23	NUM
iajs-3728	52	76	)	)	PUNCT
iajs-3728	52	77	and	and	CCONJ
iajs-3728	52	78	spectral	spectral	ADJ
iajs-3728	52	79	clustering	clustering	NOUN
iajs-3728	52	80	(	(	PUNCT
iajs-3728	52	81	11	11	NUM
iajs-3728	52	82	)	)	PUNCT
iajs-3728	52	83	.	.	PUNCT
iajs-3728	53	1	the	the	DET
iajs-3728	53	2	article	article	NOUN
iajs-3728	53	3	is	be	AUX
iajs-3728	53	4	organized	organize	VERB
iajs-3728	53	5	as	as	SCONJ
iajs-3728	53	6	follows	follow	VERB
iajs-3728	53	7	:	:	PUNCT
iajs-3728	53	8	section	section	NOUN
iajs-3728	53	9	2	2	NUM
iajs-3728	53	10	introduces	introduce	NOUN
iajs-3728	53	11	stochastic	stochastic	ADJ
iajs-3728	53	12	block	block	NOUN
iajs-3728	53	13	models	model	NOUN
iajs-3728	53	14	.	.	PUNCT
iajs-3728	54	1	section	section	NOUN
iajs-3728	54	2	3	3	NUM
iajs-3728	54	3	presents	present	VERB
iajs-3728	54	4	the	the	DET
iajs-3728	54	5	objective	objective	NOUN
iajs-3728	54	6	,	,	PUNCT
iajs-3728	54	7	and	and	CCONJ
iajs-3728	54	8	some	some	DET
iajs-3728	54	9	details	detail	NOUN
iajs-3728	54	10	on	on	ADP
iajs-3728	54	11	algorithmic	algorithmic	ADJ
iajs-3728	54	12	settings	setting	NOUN
iajs-3728	54	13	are	be	AUX
iajs-3728	54	14	also	also	ADV
iajs-3728	54	15	given	give	VERB
iajs-3728	54	16	.	.	PUNCT
iajs-3728	55	1	numerical	numerical	ADJ
iajs-3728	55	2	results	result	NOUN
iajs-3728	55	3	are	be	AUX
iajs-3728	55	4	presented	present	VERB
iajs-3728	55	5	in	in	ADP
iajs-3728	55	6	section	section	NOUN
iajs-3728	55	7	4	4	NUM
iajs-3728	55	8	that	that	PRON
iajs-3728	55	9	show	show	VERB
iajs-3728	55	10	the	the	DET
iajs-3728	55	11	performance	performance	NOUN
iajs-3728	55	12	of	of	ADP
iajs-3728	55	13	the	the	DET
iajs-3728	55	14	model	model	NOUN
iajs-3728	55	15	selection	selection	NOUN
iajs-3728	55	16	algorithm	algorithm	NOUN
iajs-3728	55	17	on	on	ADP
iajs-3728	55	18	the	the	DET
iajs-3728	55	19	rcmm	rcmm	NOUN
iajs-3728	55	20	method	method	NOUN
iajs-3728	55	21	compared	compare	VERB
iajs-3728	55	22	with	with	ADP
iajs-3728	55	23	other	other	ADJ
iajs-3728	55	24	methods	method	NOUN
iajs-3728	55	25	.	.	PUNCT
iajs-3728	56	1	finally	finally	ADV
iajs-3728	56	2	,	,	PUNCT
iajs-3728	56	3	concluding	conclude	VERB
iajs-3728	56	4	remarks	remark	NOUN
iajs-3728	56	5	are	be	AUX
iajs-3728	56	6	given	give	VERB
iajs-3728	56	7	in	in	ADP
iajs-3728	56	8	section	section	NOUN
iajs-3728	56	9	5	5	NUM
iajs-3728	56	10	.	.	PUNCT
iajs-3728	57	1	2.optimizing	2.optimizing	NUM
iajs-3728	57	2	likelihood	likelihood	NOUN
iajs-3728	57	3	function	function	NOUN
iajs-3728	57	4	for	for	ADP
iajs-3728	57	5	model	model	NOUN
iajs-3728	57	6	selection	selection	NOUN
iajs-3728	57	7	and	and	CCONJ
iajs-3728	57	8	estimation	estimation	NOUN
iajs-3728	57	9	in	in	ADP
iajs-3728	57	10	community	community	NOUN
iajs-3728	57	11	detection	detection	NOUN
iajs-3728	57	12	,	,	PUNCT
iajs-3728	57	13	the	the	DET
iajs-3728	57	14	goal	goal	NOUN
iajs-3728	57	15	of	of	ADP
iajs-3728	57	16	optimization	optimization	NOUN
iajs-3728	57	17	methods	method	NOUN
iajs-3728	57	18	with	with	ADP
iajs-3728	57	19	stochastic	stochastic	ADJ
iajs-3728	57	20	block	block	NOUN
iajs-3728	57	21	models	model	NOUN
iajs-3728	57	22	is	be	AUX
iajs-3728	57	23	to	to	PART
iajs-3728	57	24	estimate	estimate	VERB
iajs-3728	57	25	the	the	DET
iajs-3728	57	26	parameters	parameter	NOUN
iajs-3728	57	27	of	of	ADP
iajs-3728	57	28	the	the	DET
iajs-3728	57	29	model	model	NOUN
iajs-3728	57	30	,	,	PUNCT
iajs-3728	57	31	such	such	ADJ
iajs-3728	57	32	as	as	ADP
iajs-3728	57	33	the	the	DET
iajs-3728	57	34	number	number	NOUN
iajs-3728	57	35	of	of	ADP
iajs-3728	57	36	communities	community	NOUN
iajs-3728	57	37	and	and	CCONJ
iajs-3728	57	38	the	the	DET
iajs-3728	57	39	probability	probability	NOUN
iajs-3728	57	40	of	of	ADP
iajs-3728	57	41	connections	connection	NOUN
iajs-3728	57	42	between	between	ADP
iajs-3728	57	43	nodes	node	NOUN
iajs-3728	57	44	within	within	ADP
iajs-3728	57	45	and	and	CCONJ
iajs-3728	57	46	between	between	ADP
iajs-3728	57	47	communities	community	NOUN
iajs-3728	57	48	,	,	PUNCT
iajs-3728	57	49	in	in	ADP
iajs-3728	57	50	order	order	NOUN
iajs-3728	57	51	to	to	PART
iajs-3728	57	52	get	get	VERB
iajs-3728	57	53	the	the	DET
iajs-3728	57	54	optimal	optimal	ADJ
iajs-3728	57	55	membership	membership	NOUN
iajs-3728	57	56	label	label	NOUN
iajs-3728	57	57	vector	vector	NOUN
iajs-3728	57	58	z.	z.	PROPN
iajs-3728	58	1	this	this	PRON
iajs-3728	58	2	is	be	AUX
iajs-3728	58	3	done	do	VERB
iajs-3728	58	4	by	by	ADP
iajs-3728	58	5	maximizing	maximize	VERB
iajs-3728	58	6	a	a	DET
iajs-3728	58	7	likelihood	likelihood	NOUN
iajs-3728	58	8	function	function	NOUN
iajs-3728	58	9	that	that	PRON
iajs-3728	58	10	measures	measure	VERB
iajs-3728	58	11	how	how	SCONJ
iajs-3728	58	12	well	well	ADV
iajs-3728	58	13	the	the	DET
iajs-3728	58	14	model	model	NOUN
iajs-3728	58	15	fits	fit	VERB
iajs-3728	58	16	the	the	DET
iajs-3728	58	17	observed	observed	ADJ
iajs-3728	58	18	network	network	NOUN
iajs-3728	58	19	data	datum	NOUN
iajs-3728	58	20	,	,	PUNCT
iajs-3728	58	21	therefore	therefore	ADV
iajs-3728	58	22	detecting	detect	VERB
iajs-3728	58	23	the	the	DET
iajs-3728	58	24	network	network	NOUN
iajs-3728	58	25	community	community	NOUN
iajs-3728	58	26	.	.	PUNCT
iajs-3728	59	1	in	in	ADP
iajs-3728	59	2	the	the	DET
iajs-3728	59	3	stochastic	stochastic	ADJ
iajs-3728	59	4	block	block	NOUN
iajs-3728	59	5	model	model	PROPN
iajs-3728	59	6	sbm	sbm	PROPN
iajs-3728	59	7	,	,	PUNCT
iajs-3728	59	8	the	the	DET
iajs-3728	59	9	’s	’s	NOUN
iajs-3728	59	10	are	be	AUX
iajs-3728	59	11	independent	independent	ADJ
iajs-3728	59	12	bernoulli	bernoulli	NOUN
iajs-3728	59	13	variables	variable	NOUN
iajs-3728	59	14	with	with	ADP
iajs-3728	59	15	probability	probability	NOUN
iajs-3728	59	16	pij	pij	NOUN
iajs-3728	59	17	,	,	PUNCT
iajs-3728	59	18	which	which	PRON
iajs-3728	59	19	is	be	AUX
iajs-3728	59	20	the	the	DET
iajs-3728	59	21	expectation	expectation	NOUN
iajs-3728	59	22	of	of	ADP
iajs-3728	59	23	that	that	PRON
iajs-3728	59	24	is	be	AUX
iajs-3728	59	25	(	(	PUNCT
iajs-3728	59	26	)	)	PUNCT
iajs-3728	59	27	which	which	PRON
iajs-3728	59	28	performs	perform	VERB
iajs-3728	59	29	a	a	DET
iajs-3728	59	30	symmetric	symmetric	ADJ
iajs-3728	59	31	matrix	matrix	NOUN
iajs-3728	59	32	(	(	PUNCT
iajs-3728	59	33	connectivity	connectivity	NOUN
iajs-3728	59	34	matrix	matrix	NOUN
iajs-3728	59	35	)	)	PUNCT
iajs-3728	59	36	given	give	VERB
iajs-3728	59	37	the	the	DET
iajs-3728	59	38	node	node	ADJ
iajs-3728	59	39	labels	label	NOUN
iajs-3728	59	40	(	(	PUNCT
iajs-3728	59	41	the	the	DET
iajs-3728	59	42	vector	vector	NOUN
iajs-3728	59	43	z	z	PROPN
iajs-3728	59	44	)	)	PUNCT
iajs-3728	59	45	,	,	PUNCT
iajs-3728	59	46	the	the	DET
iajs-3728	59	47	likelihood	likelihood	NOUN
iajs-3728	59	48	of	of	ADP
iajs-3728	59	49	observing	observe	VERB
iajs-3728	59	50	a	a	PRON
iajs-3728	59	51	with	with	ADP
iajs-3728	59	52	probability	probability	NOUN
iajs-3728	59	53	b	b	NOUN
iajs-3728	59	54	under	under	ADP
iajs-3728	59	55	sbm	sbm	PROPN
iajs-3728	59	56	is	be	AUX
iajs-3728	59	57	:	:	PUNCT
iajs-3728	59	58	(	(	PUNCT
iajs-3728	59	59	|	|	ADV
iajs-3728	59	60	)	)	PUNCT
iajs-3728	59	61	∏	∏	PROPN
iajs-3728	59	62	(	(	PUNCT
iajs-3728	59	63	)	)	PUNCT
iajs-3728	59	64	(	(	PUNCT
iajs-3728	59	65	)	)	PUNCT
iajs-3728	59	66	(	(	PUNCT
iajs-3728	59	67	4	4	NUM
iajs-3728	59	68	)	)	PUNCT
iajs-3728	59	69	whereas	whereas	SCONJ
iajs-3728	59	70	the	the	DET
iajs-3728	59	71	dcsbm	dcsbm	NOUN
iajs-3728	59	72	,	,	PUNCT
iajs-3728	59	73	which	which	PRON
iajs-3728	59	74	limits	limit	VERB
iajs-3728	59	75	the	the	DET
iajs-3728	59	76	applicability	applicability	NOUN
iajs-3728	59	77	of	of	ADP
iajs-3728	59	78	the	the	DET
iajs-3728	59	79	sbm	sbm	NOUN
iajs-3728	59	80	,	,	PUNCT
iajs-3728	59	81	has	have	VERB
iajs-3728	59	82	an	an	DET
iajs-3728	59	83	expected	expect	VERB
iajs-3728	59	84	value	value	NOUN
iajs-3728	59	85	of	of	ADP
iajs-3728	59	86	the	the	DET
iajs-3728	59	87	adjacency	adjacency	NOUN
iajs-3728	59	88	matrix	matrix	NOUN
iajs-3728	59	89	element	element	NOUN
iajs-3728	59	90	equal	equal	ADJ
iajs-3728	59	91	to	to	ADP
iajs-3728	59	92	:	:	PUNCT
iajs-3728	59	93	(	(	PUNCT
iajs-3728	59	94	)	)	PUNCT
iajs-3728	59	95	(	(	PUNCT
iajs-3728	59	96	5	5	X
iajs-3728	59	97	)	)	PUNCT
iajs-3728	59	98	ihjpas	ihjpa	NOUN
iajs-3728	59	99	.	.	PUNCT
iajs-3728	60	1	2025	2025	NUM
iajs-3728	60	2	,	,	PUNCT
iajs-3728	60	3	38(3	38(3	NUM
iajs-3728	60	4	)	)	PUNCT
iajs-3728	60	5	328	328	NUM
iajs-3728	60	6	but	but	CCONJ
iajs-3728	60	7	a	a	DET
iajs-3728	60	8	poisson	poisson	NOUN
iajs-3728	60	9	distribution	distribution	NOUN
iajs-3728	60	10	with	with	ADP
iajs-3728	60	11	this	this	DET
iajs-3728	60	12	mean	mean	NOUN
iajs-3728	60	13	is	be	AUX
iajs-3728	60	14	used	use	VERB
iajs-3728	60	15	to	to	PART
iajs-3728	60	16	determine	determine	VERB
iajs-3728	60	17	the	the	DET
iajs-3728	60	18	actual	actual	ADJ
iajs-3728	60	19	number	number	NOUN
iajs-3728	60	20	of	of	ADP
iajs-3728	60	21	edges	edge	NOUN
iajs-3728	60	22	between	between	ADP
iajs-3728	60	23	any	any	DET
iajs-3728	60	24	two	two	NUM
iajs-3728	60	25	vertices	vertex	NOUN
iajs-3728	60	26	.	.	PUNCT
iajs-3728	61	1	the	the	DET
iajs-3728	61	2	likelihood	likelihood	NOUN
iajs-3728	61	3	function	function	NOUN
iajs-3728	61	4	is	be	AUX
iajs-3728	61	5	defined	define	VERB
iajs-3728	61	6	as	as	ADP
iajs-3728	61	7	(	(	PUNCT
iajs-3728	61	8	|	|	ADV
iajs-3728	61	9	)	)	PUNCT
iajs-3728	61	10	∏	∏	PROPN
iajs-3728	61	11	(	(	PUNCT
iajs-3728	61	12	)	)	PUNCT
iajs-3728	61	13	(	(	PUNCT
iajs-3728	61	14	)	)	PUNCT
iajs-3728	61	15	∏	∏	PROPN
iajs-3728	61	16	(	(	PUNCT
iajs-3728	61	17	)	)	PUNCT
iajs-3728	61	18	(	(	PUNCT
iajs-3728	61	19	)	)	PUNCT
iajs-3728	61	20	(	(	PUNCT
iajs-3728	61	21	)	)	PUNCT
iajs-3728	61	22	(	(	PUNCT
iajs-3728	61	23	6	6	NUM
iajs-3728	61	24	)	)	PUNCT
iajs-3728	61	25	to	to	PART
iajs-3728	61	26	find	find	VERB
iajs-3728	61	27	the	the	DET
iajs-3728	61	28	optimal	optimal	ADJ
iajs-3728	61	29	partition	partition	NOUN
iajs-3728	61	30	,	,	PUNCT
iajs-3728	61	31	the	the	DET
iajs-3728	61	32	likelihood	likelihood	NOUN
iajs-3728	61	33	function	function	NOUN
iajs-3728	61	34	is	be	AUX
iajs-3728	61	35	maximized	maximize	VERB
iajs-3728	61	36	over	over	ADP
iajs-3728	61	37	b	b	NOUN
iajs-3728	61	38	,	,	PUNCT
iajs-3728	61	39	giving	give	VERB
iajs-3728	61	40	the	the	DET
iajs-3728	61	41	profile	profile	NOUN
iajs-3728	61	42	likelihood	likelihood	NOUN
iajs-3728	61	43	criteria	criterion	NOUN
iajs-3728	61	44	to	to	PART
iajs-3728	61	45	be	be	AUX
iajs-3728	61	46	optimized	optimize	VERB
iajs-3728	61	47	over	over	ADP
iajs-3728	61	48	all	all	DET
iajs-3728	61	49	possible	possible	ADJ
iajs-3728	61	50	partitions	partition	NOUN
iajs-3728	61	51	.	.	PUNCT
iajs-3728	62	1	actually	actually	ADV
iajs-3728	62	2	,	,	PUNCT
iajs-3728	62	3	the	the	DET
iajs-3728	62	4	bernoulli	bernoulli	NOUN
iajs-3728	62	5	likelihood	likelihood	NOUN
iajs-3728	62	6	is	be	AUX
iajs-3728	62	7	replaced	replace	VERB
iajs-3728	62	8	by	by	ADP
iajs-3728	62	9	the	the	DET
iajs-3728	62	10	poisson	poisson	PROPN
iajs-3728	62	11	likelihood	likelihood	NOUN
iajs-3728	62	12	,	,	PUNCT
iajs-3728	62	13	to	to	PART
iajs-3728	62	14	simplify	simplify	VERB
iajs-3728	62	15	derivations	derivation	NOUN
iajs-3728	62	16	.	.	PUNCT
iajs-3728	63	1	so	so	ADV
iajs-3728	63	2	for	for	ADP
iajs-3728	63	3	these	these	DET
iajs-3728	63	4	two	two	NUM
iajs-3728	63	5	models	model	NOUN
iajs-3728	63	6	,	,	PUNCT
iajs-3728	63	7	a	a	DET
iajs-3728	63	8	profile	profile	NOUN
iajs-3728	63	9	likelihood	likelihood	NOUN
iajs-3728	63	10	was	be	AUX
iajs-3728	63	11	driven	drive	VERB
iajs-3728	63	12	from	from	ADP
iajs-3728	63	13	the	the	DET
iajs-3728	63	14	log	log	NOUN
iajs-3728	63	15	-	-	PUNCT
iajs-3728	63	16	likelihood	likelihood	NOUN
iajs-3728	63	17	function	function	NOUN
iajs-3728	63	18	of	of	ADP
iajs-3728	63	19	each	each	PRON
iajs-3728	63	20	of	of	ADP
iajs-3728	63	21	them	they	PRON
iajs-3728	63	22	to	to	PART
iajs-3728	63	23	give	give	VERB
iajs-3728	63	24	the	the	DET
iajs-3728	63	25	following	follow	VERB
iajs-3728	63	26	criteria	criterion	NOUN
iajs-3728	63	27	(	(	PUNCT
iajs-3728	63	28	1	1	NUM
iajs-3728	63	29	):	):	PUNCT
iajs-3728	63	30	∑	∑	PUNCT
iajs-3728	63	31	(	(	PUNCT
iajs-3728	63	32	7	7	NUM
iajs-3728	63	33	)	)	PUNCT
iajs-3728	63	34	∑	∑	PRON
iajs-3728	63	35	(	(	PUNCT
iajs-3728	63	36	8)	8)	NUM
iajs-3728	63	37	where	where	SCONJ
iajs-3728	63	38	∑	∑	PROPN
iajs-3728	63	39	*	*	PUNCT
iajs-3728	63	40	is	be	AUX
iajs-3728	63	41	the	the	DET
iajs-3728	63	42	number	number	NOUN
iajs-3728	63	43	of	of	ADP
iajs-3728	63	44	edges	edge	NOUN
iajs-3728	63	45	from	from	ADP
iajs-3728	63	46	community	community	NOUN
iajs-3728	63	47	k	k	PROPN
iajs-3728	63	48	to	to	ADP
iajs-3728	63	49	community	community	NOUN
iajs-3728	63	50	l	l	NOUN
iajs-3728	63	51	,	,	PUNCT
iajs-3728	63	52	.	.	PUNCT
iajs-3728	64	1	∑	∑	ADV
iajs-3728	64	2	be	be	AUX
iajs-3728	64	3	the	the	DET
iajs-3728	64	4	node	node	ADJ
iajs-3728	64	5	degrees	degree	NOUN
iajs-3728	64	6	in	in	ADP
iajs-3728	64	7	community	community	NOUN
iajs-3728	64	8	k.	k.	PROPN
iajs-3728	65	1	in	in	ADP
iajs-3728	65	2	fact	fact	NOUN
iajs-3728	65	3	gives	give	VERB
iajs-3728	65	4	twice	twice	DET
iajs-3728	65	5	the	the	DET
iajs-3728	65	6	number	number	NOUN
iajs-3728	65	7	of	of	ADP
iajs-3728	65	8	edgeswithin	edgeswithin	ADJ
iajs-3728	65	9	community	community	NOUN
iajs-3728	65	10	k	k	PROPN
iajs-3728	65	11	,	,	PUNCT
iajs-3728	65	12	and	and	CCONJ
iajs-3728	65	13	is	be	AUX
iajs-3728	65	14	the	the	DET
iajs-3728	65	15	number	number	NOUN
iajs-3728	65	16	of	of	ADP
iajs-3728	65	17	nodes	node	NOUN
iajs-3728	65	18	of	of	ADP
iajs-3728	65	19	community	community	NOUN
iajs-3728	65	20	k	k	NOUN
iajs-3728	65	21	,	,	PUNCT
iajs-3728	65	22	also	also	ADV
iajs-3728	65	23	and	and	CCONJ
iajs-3728	65	24	give	give	VERB
iajs-3728	65	25	the	the	DET
iajs-3728	65	26	maximum	maximum	ADJ
iajs-3728	65	27	likelihood	likelihood	NOUN
iajs-3728	65	28	values	value	NOUN
iajs-3728	65	29	̂	̂	X
iajs-3728	65	30	of	of	ADP
iajs-3728	65	31	the	the	DET
iajs-3728	65	32	sbm	sbm	NOUN
iajs-3728	65	33	and	and	CCONJ
iajs-3728	65	34	dcsbm	dcsbm	ADJ
iajs-3728	65	35	model	model	NOUN
iajs-3728	65	36	parameters	parameter	NOUN
iajs-3728	65	37	respectively	respectively	ADV
iajs-3728	65	38	.	.	PUNCT
iajs-3728	66	1	3.enhancement	3.enhancement	NUM
iajs-3728	66	2	of	of	ADP
iajs-3728	66	3	cmm	cmm	PROPN
iajs-3728	66	4	for	for	ADP
iajs-3728	66	5	community	community	NOUN
iajs-3728	66	6	detection	detection	NOUN
iajs-3728	66	7	there	there	PRON
iajs-3728	66	8	are	be	VERB
iajs-3728	66	9	many	many	ADJ
iajs-3728	66	10	methods	method	NOUN
iajs-3728	66	11	for	for	ADP
iajs-3728	66	12	detecting	detect	VERB
iajs-3728	66	13	communities	community	NOUN
iajs-3728	66	14	,	,	PUNCT
iajs-3728	66	15	distinguishing	distinguish	VERB
iajs-3728	66	16	between	between	ADP
iajs-3728	66	17	traditional	traditional	ADJ
iajs-3728	66	18	methods	method	NOUN
iajs-3728	66	19	and	and	CCONJ
iajs-3728	66	20	optimizationand	optimizationand	NOUN
iajs-3728	66	21	model	model	NOUN
iajs-3728	66	22	-	-	PUNCT
iajs-3728	66	23	based	base	VERB
iajs-3728	66	24	methods	method	NOUN
iajs-3728	66	25	.	.	PUNCT
iajs-3728	67	1	the	the	DET
iajs-3728	67	2	optimization	optimization	NOUN
iajs-3728	67	3	algorithm	algorithm	NOUN
iajs-3728	67	4	tries	try	VERB
iajs-3728	67	5	to	to	PART
iajs-3728	67	6	identify	identify	VERB
iajs-3728	67	7	communities	community	NOUN
iajs-3728	67	8	within	within	ADP
iajs-3728	67	9	the	the	DET
iajs-3728	67	10	network	network	NOUN
iajs-3728	67	11	(	(	PUNCT
iajs-3728	67	12	graph	graph	NOUN
iajs-3728	67	13	)	)	PUNCT
iajs-3728	67	14	based	base	VERB
iajs-3728	67	15	on	on	ADP
iajs-3728	67	16	the	the	DET
iajs-3728	67	17	optimization	optimization	NOUN
iajs-3728	67	18	of	of	ADP
iajs-3728	67	19	some	some	DET
iajs-3728	67	20	quality	quality	NOUN
iajs-3728	67	21	functions	function	NOUN
iajs-3728	67	22	.	.	PUNCT
iajs-3728	68	1	depending	depend	VERB
iajs-3728	68	2	on	on	ADP
iajs-3728	68	3	the	the	DET
iajs-3728	68	4	representation	representation	NOUN
iajs-3728	68	5	of	of	ADP
iajs-3728	68	6	the	the	DET
iajs-3728	68	7	network	network	NOUN
iajs-3728	68	8	,	,	PUNCT
iajs-3728	68	9	the	the	DET
iajs-3728	68	10	modularity	modularity	NOUN
iajs-3728	68	11	function	function	NOUN
iajs-3728	68	12	,	,	PUNCT
iajs-3728	68	13	denoted	denote	VERB
iajs-3728	68	14	by	by	ADP
iajs-3728	68	15	q	q	PROPN
iajs-3728	68	16	,	,	PUNCT
iajs-3728	68	17	can	can	AUX
iajs-3728	68	18	be	be	AUX
iajs-3728	68	19	defined	define	VERB
iajs-3728	68	20	as	as	SCONJ
iajs-3728	68	21	follows	follow	VERB
iajs-3728	68	22	:	:	PUNCT
iajs-3728	68	23	it	it	PRON
iajs-3728	68	24	is	be	AUX
iajs-3728	68	25	a	a	DET
iajs-3728	68	26	function	function	NOUN
iajs-3728	68	27	that	that	PRON
iajs-3728	68	28	measures	measure	VERB
iajs-3728	68	29	the	the	DET
iajs-3728	68	30	strength	strength	NOUN
iajs-3728	68	31	of	of	ADP
iajs-3728	68	32	the	the	DET
iajs-3728	68	33	partition	partition	NOUN
iajs-3728	68	34	of	of	ADP
iajs-3728	68	35	a	a	DET
iajs-3728	68	36	network	network	NOUN
iajs-3728	68	37	into	into	ADP
iajs-3728	68	38	communities	community	NOUN
iajs-3728	68	39	(	(	PUNCT
iajs-3728	68	40	subgraphs	subgraph	NOUN
iajs-3728	68	41	)	)	PUNCT
iajs-3728	68	42	considering	consider	VERB
iajs-3728	68	43	the	the	DET
iajs-3728	68	44	degree	degree	NOUN
iajs-3728	68	45	distribution	distribution	NOUN
iajs-3728	68	46	,	,	PUNCT
iajs-3728	68	47	such	such	ADJ
iajs-3728	68	48	that	that	SCONJ
iajs-3728	68	49	networks	network	NOUN
iajs-3728	68	50	with	with	ADP
iajs-3728	68	51	a	a	DET
iajs-3728	68	52	high	high	ADJ
iajs-3728	68	53	modularity	modularity	NOUN
iajs-3728	68	54	value	value	NOUN
iajs-3728	68	55	have	have	VERB
iajs-3728	68	56	dense	dense	ADJ
iajs-3728	68	57	connections	connection	NOUN
iajs-3728	68	58	between	between	ADP
iajs-3728	68	59	nodes	node	NOUN
iajs-3728	68	60	within	within	ADP
iajs-3728	68	61	a	a	DET
iajs-3728	68	62	cluster	cluster	NOUN
iajs-3728	68	63	but	but	CCONJ
iajs-3728	68	64	sparse	sparse	ADJ
iajs-3728	68	65	connections	connection	NOUN
iajs-3728	68	66	between	between	ADP
iajs-3728	68	67	nodes	node	NOUN
iajs-3728	68	68	of	of	ADP
iajs-3728	68	69	different	different	ADJ
iajs-3728	68	70	clusters	cluster	NOUN
iajs-3728	68	71	.	.	PUNCT
iajs-3728	69	1	the	the	DET
iajs-3728	69	2	modularity	modularity	NOUN
iajs-3728	69	3	function	function	NOUN
iajs-3728	69	4	is	be	AUX
iajs-3728	69	5	given	give	VERB
iajs-3728	69	6	by	by	ADP
iajs-3728	69	7	:	:	PUNCT
iajs-3728	69	8	∑	∑	PUNCT
iajs-3728	69	9	.	.	PUNCT
iajs-3728	69	10	/	/	PUNCT
iajs-3728	70	1	(	(	PUNCT
iajs-3728	70	2	)	)	PUNCT
iajs-3728	70	3	(	(	PUNCT
iajs-3728	70	4	9	9	NUM
iajs-3728	70	5	)	)	PUNCT
iajs-3728	70	6	where	where	SCONJ
iajs-3728	70	7	,	,	PUNCT
iajs-3728	70	8	.=	.=	VERB
iajs-3728	70	9	the	the	DET
iajs-3728	70	10	adjacency	adjacency	NOUN
iajs-3728	70	11	matrix	matrix	NOUN
iajs-3728	70	12	and	and	CCONJ
iajs-3728	70	13	kronecker	kronecker	NOUN
iajs-3728	70	14	delta	delta	NOUN
iajs-3728	70	15	(	(	PUNCT
iajs-3728	70	16	(	(	PUNCT
iajs-3728	70	17	,	,	PUNCT
iajs-3728	70	18	)	)	PUNCT
iajs-3728	70	19	is	be	AUX
iajs-3728	70	20	a	a	DET
iajs-3728	70	21	function	function	NOUN
iajs-3728	70	22	that	that	PRON
iajs-3728	70	23	yield	yield	VERB
iajs-3728	70	24	0	0	NUM
iajs-3728	70	25	or	or	CCONJ
iajs-3728	70	26	1	1	NUM
iajs-3728	70	27	if	if	SCONJ
iajs-3728	70	28	vertices	vertice	VERB
iajs-3728	70	29	i	i	PRON
iajs-3728	70	30	of	of	ADP
iajs-3728	70	31	degree	degree	NOUN
iajs-3728	70	32	,	,	PUNCT
iajs-3728	70	33	.and	.and	PUNCT
iajs-3728	71	1	j	j	PROPN
iajs-3728	71	2	of	of	ADP
iajs-3728	71	3	degree	degree	NOUN
iajs-3728	71	4	,	,	PUNCT
iajs-3728	71	5	.	.	PUNCT
iajs-3728	72	1	are	be	AUX
iajs-3728	72	2	in	in	ADP
iajs-3728	72	3	the	the	DET
iajs-3728	72	4	same	same	ADJ
iajs-3728	72	5	community	community	NOUN
iajs-3728	72	6	or	or	CCONJ
iajs-3728	72	7	not	not	PART
iajs-3728	72	8	)	)	PUNCT
iajs-3728	72	9	.	.	PUNCT
iajs-3728	73	1	so	so	ADV
iajs-3728	73	2	,	,	PUNCT
iajs-3728	73	3	the	the	DET
iajs-3728	73	4	possible	possible	ADJ
iajs-3728	73	5	existing	exist	VERB
iajs-3728	73	6	communities	community	NOUN
iajs-3728	73	7	are	be	AUX
iajs-3728	73	8	gained	gain	VERB
iajs-3728	73	9	by	by	ADP
iajs-3728	73	10	balancing	balance	VERB
iajs-3728	73	11	between	between	ADP
iajs-3728	73	12	the	the	DET
iajs-3728	73	13	actual	actual	ADJ
iajs-3728	73	14	density	density	NOUN
iajs-3728	73	15	of	of	ADP
iajs-3728	73	16	edges	edge	NOUN
iajs-3728	73	17	and	and	CCONJ
iajs-3728	73	18	the	the	DET
iajs-3728	73	19	expected	expect	VERB
iajs-3728	73	20	density	density	NOUN
iajs-3728	73	21	that	that	PRON
iajs-3728	73	22	would	would	AUX
iajs-3728	73	23	appear	appear	VERB
iajs-3728	73	24	in	in	ADP
iajs-3728	73	25	the	the	DET
iajs-3728	73	26	communities	community	NOUN
iajs-3728	73	27	if	if	SCONJ
iajs-3728	73	28	the	the	DET
iajs-3728	73	29	vertices	vertex	NOUN
iajs-3728	73	30	were	be	AUX
iajs-3728	73	31	connected	connect	VERB
iajs-3728	73	32	regardless	regardless	ADV
iajs-3728	73	33	of	of	ADP
iajs-3728	73	34	community	community	NOUN
iajs-3728	73	35	structure	structure	NOUN
iajs-3728	73	36	.	.	PUNCT
iajs-3728	74	1	many	many	ADJ
iajs-3728	74	2	methods	method	NOUN
iajs-3728	74	3	for	for	ADP
iajs-3728	74	4	community	community	NOUN
iajs-3728	74	5	detection	detection	NOUN
iajs-3728	74	6	are	be	AUX
iajs-3728	74	7	used	use	VERB
iajs-3728	74	8	in	in	ADP
iajs-3728	74	9	this	this	DET
iajs-3728	74	10	work	work	NOUN
iajs-3728	74	11	for	for	ADP
iajs-3728	74	12	in	in	ADP
iajs-3728	74	13	comparison	comparison	NOUN
iajs-3728	74	14	,	,	PUNCT
iajs-3728	74	15	these	these	DET
iajs-3728	74	16	methods	method	NOUN
iajs-3728	74	17	are	be	AUX
iajs-3728	74	18	summarized	summarize	VERB
iajs-3728	74	19	as	as	SCONJ
iajs-3728	74	20	follows	follow	VERB
iajs-3728	74	21	:	:	PUNCT
iajs-3728	74	22	newman	newman	PROPN
iajs-3728	74	23	modularity	modularity	PROPN
iajs-3728	74	24	maximization	maximization	PROPN
iajs-3728	74	25	:	:	PUNCT
iajs-3728	74	26	this	this	PRON
iajs-3728	74	27	is	be	AUX
iajs-3728	74	28	an	an	DET
iajs-3728	74	29	agglomerative	agglomerative	ADJ
iajs-3728	74	30	hierarchical	hierarchical	ADJ
iajs-3728	74	31	clustering	clustering	NOUN
iajs-3728	74	32	technique	technique	NOUN
iajs-3728	74	33	in	in	ADP
iajs-3728	74	34	which	which	PRON
iajs-3728	74	35	smaller	small	ADJ
iajs-3728	74	36	communities	community	NOUN
iajs-3728	74	37	of	of	ADP
iajs-3728	74	38	vertices	vertex	NOUN
iajs-3728	74	39	are	be	AUX
iajs-3728	74	40	gradually	gradually	ADV
iajs-3728	74	41	merged	merge	VERB
iajs-3728	74	42	into	into	ADP
iajs-3728	74	43	larger	large	ADJ
iajs-3728	74	44	ones	one	NOUN
iajs-3728	74	45	,	,	PUNCT
iajs-3728	74	46	leading	lead	VERB
iajs-3728	74	47	to	to	ADP
iajs-3728	74	48	an	an	DET
iajs-3728	74	49	increase	increase	NOUN
iajs-3728	74	50	in	in	ADP
iajs-3728	74	51	modularity	modularity	NOUN
iajs-3728	74	52	(	(	PUNCT
iajs-3728	74	53	22	22	NUM
iajs-3728	74	54	)	)	PUNCT
iajs-3728	74	55	.	.	PUNCT
iajs-3728	75	1	the	the	DET
iajs-3728	75	2	algorithm	algorithm	NOUN
iajs-3728	75	3	works	work	VERB
iajs-3728	75	4	as	as	SCONJ
iajs-3728	75	5	follows	follow	VERB
iajs-3728	75	6	:	:	PUNCT
iajs-3728	75	7	step	step	NOUN
iajs-3728	75	8	1	1	NUM
iajs-3728	75	9	:	:	PUNCT
iajs-3728	75	10	assign	assign	VERB
iajs-3728	75	11	each	each	PRON
iajs-3728	75	12	of	of	ADP
iajs-3728	75	13	the	the	DET
iajs-3728	75	14	n	n	PRON
iajs-3728	75	15	vertices	vertice	VERB
iajs-3728	75	16	to	to	ADP
iajs-3728	75	17	a	a	DET
iajs-3728	75	18	single	single	ADJ
iajs-3728	75	19	cluster	cluster	NOUN
iajs-3728	75	20	.	.	PUNCT
iajs-3728	76	1	step	step	NOUN
iajs-3728	76	2	2	2	NUM
iajs-3728	76	3	:	:	PUNCT
iajs-3728	76	4	reduce	reduce	VERB
iajs-3728	76	5	the	the	DET
iajs-3728	76	6	number	number	NOUN
iajs-3728	76	7	of	of	ADP
iajs-3728	76	8	clusters	cluster	NOUN
iajs-3728	76	9	by	by	ADP
iajs-3728	76	10	adding	add	VERB
iajs-3728	76	11	an	an	DET
iajs-3728	76	12	edge	edge	NOUN
iajs-3728	76	13	to	to	ADP
iajs-3728	76	14	the	the	DET
iajs-3728	76	15	set	set	NOUN
iajs-3728	76	16	of	of	ADP
iajs-3728	76	17	unconnected	unconnected	ADJ
iajs-3728	76	18	vertices	vertex	NOUN
iajs-3728	76	19	such	such	ADJ
iajs-3728	76	20	that	that	SCONJ
iajs-3728	76	21	the	the	DET
iajs-3728	76	22	resulting	result	VERB
iajs-3728	76	23	partition	partition	NOUN
iajs-3728	76	24	has	have	VERB
iajs-3728	76	25	a	a	DET
iajs-3728	76	26	modularity	modularity	NOUN
iajs-3728	76	27	equation	equation	NOUN
iajs-3728	76	28	(	(	PUNCT
iajs-3728	76	29	9	9	X
iajs-3728	76	30	)	)	PUNCT
iajs-3728	76	31	greater	great	ADJ
iajs-3728	76	32	than	than	ADP
iajs-3728	76	33	the	the	DET
iajs-3728	76	34	value	value	NOUN
iajs-3728	76	35	of	of	ADP
iajs-3728	76	36	the	the	DET
iajs-3728	76	37	previous	previous	ADJ
iajs-3728	76	38	configuration	configuration	NOUN
iajs-3728	76	39	.	.	PUNCT
iajs-3728	77	1	step	step	NOUN
iajs-3728	77	2	3	3	NUM
iajs-3728	77	3	:	:	PUNCT
iajs-3728	77	4	choose	choose	VERB
iajs-3728	77	5	the	the	DET
iajs-3728	77	6	optimal	optimal	ADJ
iajs-3728	77	7	fusion	fusion	NOUN
iajs-3728	77	8	by	by	ADP
iajs-3728	77	9	calculating	calculate	VERB
iajs-3728	77	10	the	the	DET
iajs-3728	77	11	variation	variation	NOUN
iajs-3728	77	12	δq	δq	PART
iajs-3728	77	13	step	step	VERB
iajs-3728	77	14	4	4	NUM
iajs-3728	77	15	:	:	PUNCT
iajs-3728	77	16	repeat	repeat	VERB
iajs-3728	77	17	steps	step	NOUN
iajs-3728	77	18	2	2	NUM
iajs-3728	77	19	and	and	CCONJ
iajs-3728	77	20	3	3	NUM
iajs-3728	77	21	until	until	SCONJ
iajs-3728	77	22	you	you	PRON
iajs-3728	77	23	have	have	AUX
iajs-3728	77	24	added	add	VERB
iajs-3728	77	25	all	all	DET
iajs-3728	77	26	edges	edge	NOUN
iajs-3728	77	27	and	and	CCONJ
iajs-3728	77	28	obtained	obtain	VERB
iajs-3728	77	29	one	one	NUM
iajs-3728	77	30	cluster	cluster	NOUN
iajs-3728	77	31	.	.	PUNCT
iajs-3728	78	1	save	save	VERB
iajs-3728	78	2	the	the	DET
iajs-3728	78	3	modularity	modularity	NOUN
iajs-3728	78	4	score	score	NOUN
iajs-3728	78	5	for	for	ADP
iajs-3728	78	6	each	each	DET
iajs-3728	78	7	partition	partition	NOUN
iajs-3728	78	8	.	.	PUNCT
iajs-3728	79	1	step	step	NOUN
iajs-3728	79	2	5	5	NUM
iajs-3728	79	3	:	:	PUNCT
iajs-3728	79	4	select	select	VERB
iajs-3728	79	5	the	the	DET
iajs-3728	79	6	partition	partition	NOUN
iajs-3728	79	7	with	with	ADP
iajs-3728	79	8	the	the	DET
iajs-3728	79	9	highest	high	ADJ
iajs-3728	79	10	modularity	modularity	NOUN
iajs-3728	79	11	score	score	NOUN
iajs-3728	79	12	.	.	PUNCT
iajs-3728	80	1	ihjpas	ihjpas	PROPN
iajs-3728	80	2	.	.	PUNCT
iajs-3728	81	1	2025	2025	NUM
iajs-3728	81	2	,	,	PUNCT
iajs-3728	81	3	38(3	38(3	NUM
iajs-3728	81	4	)	)	PUNCT
iajs-3728	81	5	329	329	NUM
iajs-3728	81	6	3.1	3.1	NUM
iajs-3728	81	7	.	.	PUNCT
iajs-3728	81	8	danon	danon	ADJ
iajs-3728	81	9	method	method	NOUN
iajs-3728	81	10	in	in	ADP
iajs-3728	81	11	this	this	DET
iajs-3728	81	12	method	method	NOUN
iajs-3728	81	13	,	,	PUNCT
iajs-3728	81	14	(	(	PUNCT
iajs-3728	81	15	23	23	NUM
iajs-3728	81	16	)	)	PUNCT
iajs-3728	81	17	proposed	propose	VERB
iajs-3728	81	18	to	to	PART
iajs-3728	81	19	normalize	normalize	VERB
iajs-3728	81	20	the	the	DET
iajs-3728	81	21	modularity	modularity	NOUN
iajs-3728	81	22	variation	variation	NOUN
iajs-3728	81	23	q	q	NOUN
iajs-3728	81	24	resulting	result	VERB
iajs-3728	81	25	from	from	ADP
iajs-3728	81	26	the	the	DET
iajs-3728	81	27	union	union	NOUN
iajs-3728	81	28	of	of	ADP
iajs-3728	81	29	two	two	NUM
iajs-3728	81	30	communities	community	NOUN
iajs-3728	81	31	by	by	ADP
iajs-3728	81	32	the	the	DET
iajs-3728	81	33	proportion	proportion	NOUN
iajs-3728	81	34	of	of	ADP
iajs-3728	81	35	edges	edge	NOUN
iajs-3728	81	36	belonging	belong	VERB
iajs-3728	81	37	to	to	ADP
iajs-3728	81	38	one	one	NUM
iajs-3728	81	39	of	of	ADP
iajs-3728	81	40	the	the	DET
iajs-3728	81	41	two	two	NUM
iajs-3728	81	42	communities	community	NOUN
iajs-3728	81	43	,	,	PUNCT
iajs-3728	81	44	since	since	SCONJ
iajs-3728	81	45	the	the	DET
iajs-3728	81	46	newman	newman	PROPN
iajs-3728	81	47	technique	technique	NOUN
iajs-3728	81	48	tends	tend	VERB
iajs-3728	81	49	to	to	PART
iajs-3728	81	50	form	form	VERB
iajs-3728	81	51	large	large	ADJ
iajs-3728	81	52	communities	community	NOUN
iajs-3728	81	53	at	at	ADP
iajs-3728	81	54	the	the	DET
iajs-3728	81	55	expense	expense	NOUN
iajs-3728	81	56	of	of	ADP
iajs-3728	81	57	small	small	ADJ
iajs-3728	81	58	ones	one	NOUN
iajs-3728	81	59	.	.	PUNCT
iajs-3728	82	1	thus	thus	ADV
iajs-3728	82	2	,	,	PUNCT
iajs-3728	82	3	this	this	DET
iajs-3728	82	4	approach	approach	NOUN
iajs-3728	82	5	leads	lead	VERB
iajs-3728	82	6	to	to	ADP
iajs-3728	82	7	larger	large	ADJ
iajs-3728	82	8	modularity	modularity	NOUN
iajs-3728	82	9	optima	optima	NOUN
iajs-3728	82	10	,	,	PUNCT
iajs-3728	82	11	especially	especially	ADV
iajs-3728	82	12	when	when	SCONJ
iajs-3728	82	13	the	the	DET
iajs-3728	82	14	communities	community	NOUN
iajs-3728	82	15	have	have	VERB
iajs-3728	82	16	very	very	ADV
iajs-3728	82	17	different	different	ADJ
iajs-3728	82	18	sizes	size	NOUN
iajs-3728	82	19	.	.	PUNCT
iajs-3728	83	1	3.2.convex	3.2.convex	NUM
iajs-3728	83	2	modularity	modularity	NOUN
iajs-3728	83	3	maximization	maximization	NOUN
iajs-3728	83	4	the	the	DET
iajs-3728	83	5	cmm	cmm	PROPN
iajs-3728	83	6	algorithm	algorithm	PROPN
iajs-3728	83	7	makes	make	VERB
iajs-3728	83	8	use	use	NOUN
iajs-3728	83	9	of	of	ADP
iajs-3728	83	10	convex	convex	PROPN
iajs-3728	83	11	optimization	optimization	NOUN
iajs-3728	83	12	,	,	PUNCT
iajs-3728	83	13	where	where	SCONJ
iajs-3728	83	14	it	it	PRON
iajs-3728	83	15	is	be	AUX
iajs-3728	83	16	based	base	VERB
iajs-3728	83	17	on	on	ADP
iajs-3728	83	18	a	a	DET
iajs-3728	83	19	convex	convex	ADJ
iajs-3728	83	20	programming	programming	NOUN
iajs-3728	83	21	relaxation	relaxation	NOUN
iajs-3728	83	22	of	of	ADP
iajs-3728	83	23	the	the	DET
iajs-3728	83	24	modularity	modularity	NOUN
iajs-3728	83	25	optimization	optimization	NOUN
iajs-3728	83	26	and	and	CCONJ
iajs-3728	83	27	works	work	VERB
iajs-3728	83	28	under	under	ADP
iajs-3728	83	29	block	block	NOUN
iajs-3728	83	30	models	model	NOUN
iajs-3728	83	31	(	(	PUNCT
iajs-3728	83	32	21	21	NUM
iajs-3728	83	33	)	)	PUNCT
iajs-3728	83	34	.	.	PUNCT
iajs-3728	84	1	thus	thus	ADV
iajs-3728	84	2	,	,	PUNCT
iajs-3728	84	3	so	so	SCONJ
iajs-3728	84	4	this	this	DET
iajs-3728	84	5	algorithm	algorithm	NOUN
iajs-3728	84	6	tends	tend	VERB
iajs-3728	84	7	to	to	PART
iajs-3728	84	8	maximize	maximize	VERB
iajs-3728	84	9	modularity	modularity	NOUN
iajs-3728	84	10	by	by	ADP
iajs-3728	84	11	solving	solve	VERB
iajs-3728	84	12	the	the	DET
iajs-3728	84	13	following	follow	VERB
iajs-3728	84	14	optimization	optimization	NOUN
iajs-3728	84	15	problem	problem	NOUN
iajs-3728	84	16	:	:	PUNCT
iajs-3728	84	17	⟨	⟨	VERB
iajs-3728	84	18	⟩	⟩	NOUN
iajs-3728	84	19	(	(	PUNCT
iajs-3728	84	20	)	)	PUNCT
iajs-3728	84	21	(	(	PUNCT
iajs-3728	84	22	10	10	NUM
iajs-3728	84	23	)	)	PUNCT
iajs-3728	84	24	where	where	SCONJ
iajs-3728	84	25	x	x	PRON
iajs-3728	84	26	is	be	AUX
iajs-3728	84	27	the	the	DET
iajs-3728	84	28	partitioning	partition	VERB
iajs-3728	84	29	matrix	matrix	NOUN
iajs-3728	84	30	for	for	ADP
iajs-3728	84	31	the	the	DET
iajs-3728	84	32	network	network	NOUN
iajs-3728	84	33	nodes	nod	VERB
iajs-3728	84	34	.	.	PUNCT
iajs-3728	85	1	the	the	DET
iajs-3728	85	2	cmm	cmm	PROPN
iajs-3728	85	3	approach	approach	NOUN
iajs-3728	85	4	combines	combine	VERB
iajs-3728	85	5	convex	convex	NOUN
iajs-3728	85	6	optimization	optimization	NOUN
iajs-3728	85	7	techniques	technique	NOUN
iajs-3728	85	8	with	with	ADP
iajs-3728	85	9	modularity	modularity	NOUN
iajs-3728	85	10	maximization	maximization	NOUN
iajs-3728	85	11	and	and	CCONJ
iajs-3728	85	12	a	a	DET
iajs-3728	85	13	weighted	weight	VERB
iajs-3728	85	14	ℓ1	ℓ1	VERB
iajs-3728	85	15	-	-	PUNCT
iajs-3728	85	16	norm	norm	NOUN
iajs-3728	85	17	kmedoids	kmedoid	NOUN
iajs-3728	85	18	as	as	SCONJ
iajs-3728	85	19	follows	follow	VERB
iajs-3728	85	20	.	.	PUNCT
iajs-3728	86	1	after	after	ADP
iajs-3728	86	2	obtaining	obtain	VERB
iajs-3728	86	3	the	the	DET
iajs-3728	86	4	optimal	optimal	ADJ
iajs-3728	86	5	solution	solution	NOUN
iajs-3728	86	6	to	to	ADP
iajs-3728	86	7	equation	equation	NOUN
iajs-3728	86	8	(	(	PUNCT
iajs-3728	86	9	10	10	NUM
iajs-3728	86	10	)	)	PUNCT
iajs-3728	86	11	,	,	PUNCT
iajs-3728	86	12	̂	̂	VERB
iajs-3728	86	13	,	,	PUNCT
iajs-3728	86	14	by	by	ADP
iajs-3728	86	15	the	the	DET
iajs-3728	86	16	alternating	alternate	VERB
iajs-3728	86	17	direction	direction	NOUN
iajs-3728	86	18	method	method	NOUN
iajs-3728	86	19	of	of	ADP
iajs-3728	86	20	multipliers	multiplier	NOUN
iajs-3728	86	21	(	(	PUNCT
iajs-3728	86	22	admm	admm	PROPN
iajs-3728	86	23	)	)	PUNCT
iajs-3728	86	24	,	,	PUNCT
iajs-3728	86	25	the	the	DET
iajs-3728	86	26	communities	community	NOUN
iajs-3728	86	27	is	be	AUX
iajs-3728	86	28	extracted	extract	VERB
iajs-3728	86	29	from	from	ADP
iajs-3728	86	30	weighted	weight	VERB
iajs-3728	86	31	̂	̂	PUNCT
iajs-3728	86	32	by	by	ADP
iajs-3728	86	33	weighted	weight	VERB
iajs-3728	86	34	ℓ1	ℓ1	NOUN
iajs-3728	86	35	-	-	PUNCT
iajs-3728	86	36	norm	norm	NOUN
iajs-3728	86	37	k	k	PROPN
iajs-3728	86	38	-	-	NOUN
iajs-3728	86	39	medoids	medoid	NOUN
iajs-3728	86	40	,	,	PUNCT
iajs-3728	86	41	i.e	i.e	X
iajs-3728	86	42	by	by	ADP
iajs-3728	86	43	solving	solve	VERB
iajs-3728	86	44	the	the	DET
iajs-3728	86	45	optimization	optimization	NOUN
iajs-3728	86	46	:	:	PUNCT
iajs-3728	86	47	∑	∑	ADP
iajs-3728	86	48	∑	∑	PUNCT
iajs-3728	86	49	‖	‖	PROPN
iajs-3728	86	50	̂	̂	PUNCT
iajs-3728	86	51	‖	‖	PROPN
iajs-3728	86	52	∈	∈	PROPN
iajs-3728	86	53	∈	∈	PROPN
iajs-3728	86	54	(	(	PUNCT
iajs-3728	86	55	̂	̂	NUM
iajs-3728	86	56	)	)	PUNCT
iajs-3728	86	57	∀	∀	PUNCT
iajs-3728	87	1	∈	∈	NOUN
iajs-3728	87	2	*	*	PUNCT
iajs-3728	88	1	+	+	PUNCT
iajs-3728	88	2	(	(	PUNCT
iajs-3728	88	3	11	11	NUM
iajs-3728	88	4	)	)	PUNCT
iajs-3728	88	5	where	where	SCONJ
iajs-3728	88	6	̂	̂	PUNCT
iajs-3728	88	7	̂	̂	NUM
iajs-3728	88	8	,	,	PUNCT
iajs-3728	88	9	(	(	PUNCT
iajs-3728	88	10	)	)	PUNCT
iajs-3728	88	11	and	and	CCONJ
iajs-3728	88	12	are	be	AUX
iajs-3728	88	13	the	the	DET
iajs-3728	88	14	centers	center	NOUN
iajs-3728	88	15	,	,	PUNCT
iajs-3728	88	16	so	so	SCONJ
iajs-3728	88	17	the	the	DET
iajs-3728	88	18	clustering	clustering	NOUN
iajs-3728	88	19	is	be	AUX
iajs-3728	88	20	performed	perform	VERB
iajs-3728	88	21	on	on	ADP
iajs-3728	88	22	the	the	DET
iajs-3728	88	23	rows	row	NOUN
iajs-3728	88	24	of	of	ADP
iajs-3728	88	25	̂	̂	PUNCT
iajs-3728	88	26	instead	instead	ADV
iajs-3728	88	27	of	of	ADP
iajs-3728	88	28	̂	̂	NUM
iajs-3728	88	29	,	,	PUNCT
iajs-3728	88	30	thus	thus	ADV
iajs-3728	88	31	,	,	PUNCT
iajs-3728	88	32	this	this	DET
iajs-3728	88	33	procedure	procedure	NOUN
iajs-3728	88	34	is	be	AUX
iajs-3728	88	35	summarized	summarize	VERB
iajs-3728	88	36	as	as	SCONJ
iajs-3728	88	37	follows	follow	VERB
iajs-3728	88	38	:	:	PUNCT
iajs-3728	88	39	step	step	NOUN
iajs-3728	88	40	1	1	NUM
iajs-3728	88	41	:	:	PUNCT
iajs-3728	88	42	solve	solve	VERB
iajs-3728	88	43	the	the	DET
iajs-3728	88	44	optimization	optimization	NOUN
iajs-3728	88	45	problem	problem	NOUN
iajs-3728	88	46	(	(	PUNCT
iajs-3728	88	47	10	10	NUM
iajs-3728	88	48	)	)	PUNCT
iajs-3728	88	49	for	for	ADP
iajs-3728	88	50	x	x	PUNCT
iajs-3728	88	51	using	use	VERB
iajs-3728	88	52	the	the	DET
iajs-3728	88	53	alternate	alternate	ADJ
iajs-3728	88	54	direction	direction	NOUN
iajs-3728	88	55	method	method	NOUN
iajs-3728	88	56	of	of	ADP
iajs-3728	88	57	multipliers	multiplier	NOUN
iajs-3728	88	58	(	(	PUNCT
iajs-3728	88	59	22	22	NUM
iajs-3728	88	60	)	)	PUNCT
iajs-3728	88	61	.	.	PUNCT
iajs-3728	89	1	step	step	NOUN
iajs-3728	89	2	2	2	NUM
iajs-3728	89	3	:	:	PUNCT
iajs-3728	89	4	find	find	VERB
iajs-3728	89	5	the	the	DET
iajs-3728	89	6	k	k	NOUN
iajs-3728	89	7	-	-	NOUN
iajs-3728	89	8	partition	partition	NOUN
iajs-3728	89	9	of	of	ADP
iajs-3728	89	10	n	n	PRON
iajs-3728	89	11	nodes	node	NOUN
iajs-3728	89	12	by	by	ADP
iajs-3728	89	13	the	the	DET
iajs-3728	89	14	k	k	PROPN
iajs-3728	89	15	-	-	PUNCT
iajs-3728	89	16	medoids	medoid	NOUN
iajs-3728	89	17	approach	approach	NOUN
iajs-3728	89	18	,	,	PUNCT
iajs-3728	89	19	weighted	weight	VERB
iajs-3728	89	20	by	by	ADP
iajs-3728	89	21	node	node	ADJ
iajs-3728	89	22	degrees	degree	NOUN
iajs-3728	89	23	.	.	PUNCT
iajs-3728	90	1	3.3	3.3	NUM
iajs-3728	90	2	.	.	PUNCT
iajs-3728	90	3	regularized	regularize	VERB
iajs-3728	90	4	convex	convex	ADJ
iajs-3728	90	5	modularity	modularity	NOUN
iajs-3728	90	6	maximization	maximization	NOUN
iajs-3728	90	7	the	the	DET
iajs-3728	90	8	rcmm	rcmm	PROPN
iajs-3728	90	9	algorithm	algorithm	NOUN
iajs-3728	90	10	is	be	AUX
iajs-3728	90	11	considered	consider	VERB
iajs-3728	90	12	a	a	DET
iajs-3728	90	13	regularization	regularization	NOUN
iajs-3728	90	14	of	of	ADP
iajs-3728	90	15	the	the	DET
iajs-3728	90	16	cmm	cmm	PROPN
iajs-3728	90	17	algorithm	algorithm	PROPN
iajs-3728	90	18	.	.	PUNCT
iajs-3728	91	1	where	where	SCONJ
iajs-3728	91	2	the	the	DET
iajs-3728	91	3	common	common	ADJ
iajs-3728	91	4	neighbor	neighbor	NOUN
iajs-3728	91	5	’s	’s	PART
iajs-3728	91	6	similarity	similarity	NOUN
iajs-3728	91	7	measurement	measurement	NOUN
iajs-3728	91	8	is	be	AUX
iajs-3728	91	9	used	use	VERB
iajs-3728	91	10	to	to	PART
iajs-3728	91	11	weight	weight	VERB
iajs-3728	91	12	the	the	DET
iajs-3728	91	13	ℓ1	ℓ1	NOUN
iajs-3728	91	14	-	-	PUNCT
iajs-3728	91	15	norm	norm	NOUN
iajs-3728	91	16	of	of	ADP
iajs-3728	91	17	k	k	ADJ
iajs-3728	91	18	-	-	ADJ
iajs-3728	91	19	mediod	mediod	ADJ
iajs-3728	91	20	clustering	clustering	NOUN
iajs-3728	91	21	(	(	PUNCT
iajs-3728	91	22	20	20	NUM
iajs-3728	91	23	)	)	PUNCT
iajs-3728	91	24	instead	instead	ADV
iajs-3728	91	25	by	by	ADP
iajs-3728	91	26	node	node	ADJ
iajs-3728	91	27	degrees	degree	NOUN
iajs-3728	91	28	.	.	PUNCT
iajs-3728	92	1	where	where	SCONJ
iajs-3728	92	2	the	the	DET
iajs-3728	92	3	weight	weight	NOUN
iajs-3728	92	4	was	be	AUX
iajs-3728	92	5	given	give	VERB
iajs-3728	92	6	twice	twice	ADV
iajs-3728	92	7	,	,	PUNCT
iajs-3728	92	8	once	once	ADV
iajs-3728	92	9	to	to	ADP
iajs-3728	92	10	the	the	DET
iajs-3728	92	11	rows	row	NOUN
iajs-3728	92	12	of	of	ADP
iajs-3728	92	13	the	the	DET
iajs-3728	92	14	partition	partition	NOUN
iajs-3728	92	15	matrix	matrix	NOUN
iajs-3728	92	16	x	x	PUNCT
iajs-3728	92	17	and	and	CCONJ
iajs-3728	92	18	secondly	secondly	ADV
iajs-3728	92	19	to	to	ADP
iajs-3728	92	20	the	the	DET
iajs-3728	92	21	terms	term	NOUN
iajs-3728	92	22	of	of	ADP
iajs-3728	92	23	the	the	DET
iajs-3728	92	24	ℓ1	ℓ1	NOUN
iajs-3728	92	25	-	-	PUNCT
iajs-3728	92	26	norm	norm	NOUN
iajs-3728	92	27	of	of	ADP
iajs-3728	92	28	k	k	NOUN
iajs-3728	92	29	-	-	PUNCT
iajs-3728	92	30	medoides	medoide	NOUN
iajs-3728	92	31	.	.	PUNCT
iajs-3728	93	1	the	the	DET
iajs-3728	93	2	number	number	NOUN
iajs-3728	93	3	of	of	ADP
iajs-3728	93	4	common	common	ADJ
iajs-3728	93	5	neighbors	neighbor	NOUN
iajs-3728	93	6	between	between	ADP
iajs-3728	93	7	two	two	NUM
iajs-3728	93	8	vertices	vertex	NOUN
iajs-3728	93	9	i	i	PRON
iajs-3728	93	10	and	and	CCONJ
iajs-3728	93	11	j	j	PROPN
iajs-3728	93	12	is	be	AUX
iajs-3728	93	13	defined	define	VERB
iajs-3728	93	14	as	as	ADP
iajs-3728	93	15	∑	∑	PROPN
iajs-3728	93	16	(	(	PUNCT
iajs-3728	93	17	12	12	NUM
iajs-3728	93	18	)	)	PUNCT
iajs-3728	93	19	so	so	SCONJ
iajs-3728	93	20	the	the	DET
iajs-3728	93	21	weighted	weight	VERB
iajs-3728	93	22	ℓ1	ℓ1	NOUN
iajs-3728	93	23	-	-	PUNCT
iajs-3728	93	24	norm	norm	NOUN
iajs-3728	93	25	k	k	PROPN
iajs-3728	93	26	-	-	PUNCT
iajs-3728	93	27	medoids	medoid	NOUN
iajs-3728	93	28	clustering	clustering	NOUN
iajs-3728	93	29	will	will	AUX
iajs-3728	93	30	be	be	AUX
iajs-3728	93	31	done	do	VERB
iajs-3728	93	32	for	for	ADP
iajs-3728	93	33	the	the	DET
iajs-3728	93	34	matrix	matrix	NOUN
iajs-3728	93	35	̂	̂	PUNCT
iajs-3728	93	36	̂	̂	PUNCT
iajs-3728	93	37	and	and	CCONJ
iajs-3728	93	38	the	the	DET
iajs-3728	93	39	partition	partition	NOUN
iajs-3728	93	40	yields	yield	NOUN
iajs-3728	93	41	by	by	ADP
iajs-3728	93	42	solving	solve	VERB
iajs-3728	93	43	the	the	DET
iajs-3728	93	44	optimization	optimization	NOUN
iajs-3728	93	45	problem	problem	NOUN
iajs-3728	93	46	:	:	PUNCT
iajs-3728	93	47	∑	∑	ADP
iajs-3728	93	48	∑	∑	PUNCT
iajs-3728	93	49	‖	‖	PROPN
iajs-3728	93	50	̂	̂	PUNCT
iajs-3728	93	51	‖	‖	PROPN
iajs-3728	93	52	∈	∈	PROPN
iajs-3728	93	53	∈	∈	PROPN
iajs-3728	93	54	(	(	PUNCT
iajs-3728	93	55	̂	̂	X
iajs-3728	93	56	)	)	PUNCT
iajs-3728	93	57	∈	∈	NOUN
iajs-3728	93	58	*	*	PUNCT
iajs-3728	94	1	+	+	PUNCT
iajs-3728	94	2	(	(	PUNCT
iajs-3728	94	3	13	13	NUM
iajs-3728	94	4	)	)	PUNCT
iajs-3728	94	5	3.4	3.4	NUM
iajs-3728	94	6	.	.	PUNCT
iajs-3728	95	1	spectral	spectral	ADJ
iajs-3728	95	2	clustering	clustering	NOUN
iajs-3728	95	3	in	in	ADP
iajs-3728	95	4	general	general	ADJ
iajs-3728	95	5	it	it	PRON
iajs-3728	95	6	is	be	AUX
iajs-3728	95	7	a	a	DET
iajs-3728	95	8	method	method	NOUN
iajs-3728	95	9	uses	use	NOUN
iajs-3728	95	10	of	of	ADP
iajs-3728	95	11	information	information	NOUN
iajs-3728	95	12	which	which	PRON
iajs-3728	95	13	is	be	AUX
iajs-3728	95	14	taken	take	VERB
iajs-3728	95	15	from	from	ADP
iajs-3728	95	16	the	the	DET
iajs-3728	95	17	eigenvalues	eigenvalue	NOUN
iajs-3728	95	18	of	of	ADP
iajs-3728	95	19	special	special	ADJ
iajs-3728	95	20	matrices	matrix	NOUN
iajs-3728	95	21	like	like	ADP
iajs-3728	95	22	affinity	affinity	NOUN
iajs-3728	95	23	matrix	matrix	NOUN
iajs-3728	95	24	,	,	PUNCT
iajs-3728	95	25	degree	degree	NOUN
iajs-3728	95	26	matrix	matrix	NOUN
iajs-3728	95	27	and	and	CCONJ
iajs-3728	95	28	laplacian	laplacian	ADJ
iajs-3728	95	29	matrix	matrix	NOUN
iajs-3728	95	30	derived	derive	VERB
iajs-3728	95	31	from	from	ADP
iajs-3728	95	32	the	the	DET
iajs-3728	95	33	data	data	NOUN
iajs-3728	95	34	set	set	VERB
iajs-3728	95	35	(	(	PUNCT
iajs-3728	95	36	graph	graph	NOUN
iajs-3728	95	37	)	)	PUNCT
iajs-3728	95	38	.	.	PUNCT
iajs-3728	96	1	it	it	PRON
iajs-3728	96	2	takes	take	VERB
iajs-3728	96	3	the	the	DET
iajs-3728	96	4	matrix	matrix	NOUN
iajs-3728	96	5	of	of	ADP
iajs-3728	96	6	eigenvectors	eigenvector	NOUN
iajs-3728	96	7	associated	associate	VERB
iajs-3728	96	8	with	with	ADP
iajs-3728	96	9	the	the	DET
iajs-3728	96	10	k	k	ADV
iajs-3728	96	11	largest	large	ADJ
iajs-3728	96	12	eigenvalues	eigenvalue	NOUN
iajs-3728	96	13	of	of	ADP
iajs-3728	96	14	one	one	NUM
iajs-3728	96	15	of	of	ADP
iajs-3728	96	16	these	these	DET
iajs-3728	96	17	special	special	ADJ
iajs-3728	96	18	matrices	matrix	NOUN
iajs-3728	96	19	and	and	CCONJ
iajs-3728	96	20	performs	perform	VERB
iajs-3728	96	21	one	one	NUM
iajs-3728	96	22	of	of	ADP
iajs-3728	96	23	the	the	DET
iajs-3728	96	24	distance	distance	NOUN
iajs-3728	96	25	-	-	PUNCT
iajs-3728	96	26	based	base	VERB
iajs-3728	96	27	clustering	clustering	ADJ
iajs-3728	96	28	methods	method	NOUN
iajs-3728	96	29	on	on	ADP
iajs-3728	96	30	it	it	PRON
iajs-3728	96	31	(	(	PUNCT
iajs-3728	96	32	25	25	NUM
iajs-3728	96	33	)	)	PUNCT
iajs-3728	96	34	.	.	PUNCT
iajs-3728	97	1	so	so	ADV
iajs-3728	97	2	mathematically	mathematically	ADV
iajs-3728	97	3	,	,	PUNCT
iajs-3728	97	4	it	it	PRON
iajs-3728	97	5	is	be	AUX
iajs-3728	97	6	summarized	summarize	VERB
iajs-3728	97	7	in	in	ADP
iajs-3728	97	8	the	the	DET
iajs-3728	97	9	following	follow	VERB
iajs-3728	97	10	steps	step	NOUN
iajs-3728	97	11	:	:	PUNCT
iajs-3728	97	12	step	step	NOUN
iajs-3728	97	13	1	1	NUM
iajs-3728	97	14	:	:	PUNCT
iajs-3728	97	15	take	take	VERB
iajs-3728	97	16	the	the	DET
iajs-3728	97	17	leading	lead	VERB
iajs-3728	97	18	eigenvectors	eigenvector	NOUN
iajs-3728	97	19	,	,	PUNCT
iajs-3728	97	20	∈	∈	PROPN
iajs-3728	97	21	of	of	ADP
iajs-3728	97	22	the	the	DET
iajs-3728	97	23	adjacency	adjacency	NOUN
iajs-3728	97	24	a	a	PRON
iajs-3728	97	25	or	or	CCONJ
iajs-3728	97	26	its	its	PRON
iajs-3728	97	27	graph	graph	NOUN
iajs-3728	97	28	normalized	normalize	VERB
iajs-3728	97	29	laplacian	laplacian	ADJ
iajs-3728	97	30	l(a	l(a	PROPN
iajs-3728	97	31	)	)	PUNCT
iajs-3728	97	32	=	=	SYM
iajs-3728	98	1	d	d	NUM
iajs-3728	98	2	−1/2	−1/2	VERB
iajs-3728	98	3	ad	ad	NOUN
iajs-3728	98	4	−1/2	−1/2	NOUN
iajs-3728	98	5	,	,	PUNCT
iajs-3728	98	6	where	where	SCONJ
iajs-3728	98	7	d	d	PROPN
iajs-3728	98	8	=	=	SYM
iajs-3728	98	9	diag(di	diag(di	PROPN
iajs-3728	98	10	)	)	PUNCT
iajs-3728	98	11	is	be	AUX
iajs-3728	98	12	the	the	DET
iajs-3728	98	13	diagonal	diagonal	ADJ
iajs-3728	98	14	ihjpas	ihjpa	NOUN
iajs-3728	98	15	.	.	PUNCT
iajs-3728	99	1	2025	2025	NUM
iajs-3728	99	2	,	,	PUNCT
iajs-3728	99	3	38(3	38(3	NUM
iajs-3728	99	4	)	)	PUNCT
iajs-3728	99	5	330	330	NUM
iajs-3728	99	6	matrix	matrix	NOUN
iajs-3728	99	7	of	of	ADP
iajs-3728	99	8	degrees	degree	NOUN
iajs-3728	99	9	and	and	CCONJ
iajs-3728	99	10	the	the	DET
iajs-3728	99	11	eigenvectors	eigenvector	NOUN
iajs-3728	99	12	are	be	AUX
iajs-3728	99	13	associated	associate	VERB
iajs-3728	99	14	with	with	ADP
iajs-3728	99	15	the	the	DET
iajs-3728	99	16	k	k	ADV
iajs-3728	99	17	largest	large	ADJ
iajs-3728	99	18	eigenvalues	eigenvalue	NOUN
iajs-3728	99	19	of	of	ADP
iajs-3728	99	20	the	the	DET
iajs-3728	99	21	chosen	choose	VERB
iajs-3728	99	22	matrix	matrix	NOUN
iajs-3728	99	23	.	.	PUNCT
iajs-3728	100	1	step	step	NOUN
iajs-3728	100	2	2	2	NUM
iajs-3728	100	3	:	:	PUNCT
iajs-3728	100	4	normalize	normalize	VERB
iajs-3728	100	5	each	each	DET
iajs-3728	100	6	rows	row	NOUN
iajs-3728	100	7	of	of	ADP
iajs-3728	100	8	the	the	DET
iajs-3728	100	9	matrix	matrix	NOUN
iajs-3728	100	10	,	,	PUNCT
iajs-3728	100	11	to	to	PART
iajs-3728	100	12	find	find	VERB
iajs-3728	100	13	with	with	ADP
iajs-3728	100	14	elements	element	NOUN
iajs-3728	100	15	(	(	PUNCT
iajs-3728	100	16	∑	∑	PROPN
iajs-3728	100	17	)	)	PUNCT
iajs-3728	100	18	.	.	PUNCT
iajs-3728	101	1	step	step	NOUN
iajs-3728	101	2	3	3	NUM
iajs-3728	101	3	:	:	PUNCT
iajs-3728	101	4	run	run	VERB
iajs-3728	101	5	any	any	DET
iajs-3728	101	6	distance	distance	NOUN
iajs-3728	101	7	clustering	clustering	NOUN
iajs-3728	101	8	method	method	NOUN
iajs-3728	101	9	(	(	PUNCT
iajs-3728	101	10	e.g.	e.g.	ADV
iajs-3728	101	11	,	,	PUNCT
iajs-3728	101	12	k	k	NOUN
iajs-3728	101	13	-	-	PUNCT
iajs-3728	101	14	means	mean	NOUN
iajs-3728	101	15	)	)	PUNCT
iajs-3728	101	16	on	on	ADP
iajs-3728	101	17	the	the	DET
iajs-3728	101	18	rows	row	NOUN
iajs-3728	101	19	of	of	ADP
iajs-3728	101	20	the	the	DET
iajs-3728	101	21	resulted	result	VERB
iajs-3728	101	22	vectors	vector	NOUN
iajs-3728	101	23	to	to	PART
iajs-3728	101	24	partition	partition	VERB
iajs-3728	101	25	them	they	PRON
iajs-3728	101	26	into	into	ADP
iajs-3728	101	27	desired	desire	VERB
iajs-3728	101	28	number	number	NOUN
iajs-3728	101	29	of	of	ADP
iajs-3728	101	30	clusters	cluster	NOUN
iajs-3728	101	31	(	(	PUNCT
iajs-3728	101	32	k	k	NOUN
iajs-3728	101	33	)	)	PUNCT
iajs-3728	101	34	,	,	PUNCT
iajs-3728	101	35	where	where	SCONJ
iajs-3728	101	36	the	the	DET
iajs-3728	101	37	output	output	NOUN
iajs-3728	101	38	and	and	CCONJ
iajs-3728	101	39	the	the	DET
iajs-3728	101	40	node	node	NOUN
iajs-3728	101	41	i	i	PRON
iajs-3728	101	42	is	be	AUX
iajs-3728	101	43	assigned	assign	VERB
iajs-3728	101	44	to	to	ADP
iajs-3728	101	45	cluster	cluster	NOUN
iajs-3728	101	46	r	r	NOUN
iajs-3728	101	47	if	if	SCONJ
iajs-3728	101	48	the	the	DET
iajs-3728	101	49	i'’th	i'’th	PROPN
iajs-3728	101	50	row	row	NOUN
iajs-3728	101	51	of	of	ADP
iajs-3728	101	52	is	be	AUX
iajs-3728	101	53	assigned	assign	VERB
iajs-3728	101	54	to	to	ADP
iajs-3728	101	55	cluster	cluster	VERB
iajs-3728	101	56	.	.	PUNCT
iajs-3728	102	1	4	4	X
iajs-3728	102	2	.	.	NOUN
iajs-3728	102	3	model	model	NOUN
iajs-3728	102	4	selection	selection	NOUN
iajs-3728	102	5	model	model	NOUN
iajs-3728	102	6	selection	selection	NOUN
iajs-3728	102	7	for	for	ADP
iajs-3728	102	8	sbms	sbms	NOUN
iajs-3728	102	9	involves	involve	NOUN
iajs-3728	102	10	choosing	choose	VERB
iajs-3728	102	11	not	not	PART
iajs-3728	102	12	only	only	ADV
iajs-3728	102	13	the	the	DET
iajs-3728	102	14	marginal	marginal	ADJ
iajs-3728	102	15	probabilities	probability	NOUN
iajs-3728	102	16	but	but	CCONJ
iajs-3728	102	17	also	also	ADV
iajs-3728	102	18	the	the	DET
iajs-3728	102	19	number	number	NOUN
iajs-3728	102	20	of	of	ADP
iajs-3728	102	21	communities	community	NOUN
iajs-3728	102	22	k	k	PROPN
iajs-3728	102	23	in	in	ADP
iajs-3728	102	24	order	order	NOUN
iajs-3728	102	25	to	to	PART
iajs-3728	102	26	determine	determine	VERB
iajs-3728	102	27	the	the	DET
iajs-3728	102	28	best	good	ADJ
iajs-3728	102	29	fitting	fitting	ADJ
iajs-3728	102	30	model	model	NOUN
iajs-3728	102	31	from	from	ADP
iajs-3728	102	32	a	a	DET
iajs-3728	102	33	set	set	NOUN
iajs-3728	102	34	of	of	ADP
iajs-3728	102	35	candidate	candidate	NOUN
iajs-3728	102	36	models	model	NOUN
iajs-3728	102	37	(	(	PUNCT
iajs-3728	102	38	26	26	NUM
iajs-3728	102	39	)	)	PUNCT
iajs-3728	102	40	.	.	PUNCT
iajs-3728	103	1	this	this	PRON
iajs-3728	103	2	is	be	AUX
iajs-3728	103	3	done	do	VERB
iajs-3728	103	4	in	in	ADP
iajs-3728	103	5	two	two	NUM
iajs-3728	103	6	stages	stage	NOUN
iajs-3728	103	7	:	:	PUNCT
iajs-3728	103	8	in	in	ADP
iajs-3728	103	9	the	the	DET
iajs-3728	103	10	first	first	ADJ
iajs-3728	103	11	,	,	PUNCT
iajs-3728	103	12	the	the	DET
iajs-3728	103	13	parameters	parameter	NOUN
iajs-3728	103	14	for	for	ADP
iajs-3728	103	15	the	the	DET
iajs-3728	103	16	likelihood	likelihood	NOUN
iajs-3728	103	17	function	function	NOUN
iajs-3728	103	18	are	be	AUX
iajs-3728	103	19	estimated	estimate	VERB
iajs-3728	103	20	for	for	ADP
iajs-3728	103	21	many	many	ADJ
iajs-3728	103	22	chosen	choose	VERB
iajs-3728	103	23	k	k	PROPN
iajs-3728	103	24	,	,	PUNCT
iajs-3728	103	25	while	while	SCONJ
iajs-3728	103	26	in	in	ADP
iajs-3728	103	27	the	the	DET
iajs-3728	103	28	second	second	ADJ
iajs-3728	103	29	,	,	PUNCT
iajs-3728	103	30	the	the	DET
iajs-3728	103	31	model	model	NOUN
iajs-3728	103	32	that	that	PRON
iajs-3728	103	33	best	well	ADV
iajs-3728	103	34	fits	fit	VERB
iajs-3728	103	35	the	the	DET
iajs-3728	103	36	observed	observe	VERB
iajs-3728	103	37	data	data	NOUN
iajs-3728	103	38	is	be	AUX
iajs-3728	103	39	selected	select	VERB
iajs-3728	103	40	,	,	PUNCT
iajs-3728	103	41	i.e.	i.e.	X
iajs-3728	103	42	,	,	PUNCT
iajs-3728	103	43	.	.	PUNCT
iajs-3728	104	1	the	the	DET
iajs-3728	104	2	model	model	NOUN
iajs-3728	104	3	that	that	PRON
iajs-3728	104	4	has	have	VERB
iajs-3728	104	5	the	the	DET
iajs-3728	104	6	minimum	minimum	NOUN
iajs-3728	104	7	aic	aic	PROPN
iajs-3728	104	8	or	or	CCONJ
iajs-3728	104	9	bic	bic	PROPN
iajs-3728	104	10	value	value	NOUN
iajs-3728	104	11	(	(	PUNCT
iajs-3728	104	12	27	27	NUM
iajs-3728	104	13	-	-	SYM
iajs-3728	104	14	29	29	NUM
iajs-3728	104	15	)	)	PUNCT
iajs-3728	104	16	.	.	PUNCT
iajs-3728	105	1	in	in	ADP
iajs-3728	105	2	likelihood	likelihood	NOUN
iajs-3728	105	3	-	-	PUNCT
iajs-3728	105	4	based	base	VERB
iajs-3728	105	5	methods	method	NOUN
iajs-3728	105	6	,	,	PUNCT
iajs-3728	105	7	the	the	DET
iajs-3728	105	8	likelihood	likelihood	NOUN
iajs-3728	105	9	of	of	ADP
iajs-3728	105	10	the	the	DET
iajs-3728	105	11	observed	observed	ADJ
iajs-3728	105	12	network	network	NOUN
iajs-3728	105	13	is	be	AUX
iajs-3728	105	14	maximized	maximize	VERB
iajs-3728	105	15	among	among	ADP
iajs-3728	105	16	different	different	ADJ
iajs-3728	105	17	sbm	sbm	NOUN
iajs-3728	105	18	models	model	NOUN
iajs-3728	105	19	with	with	ADP
iajs-3728	105	20	different	different	ADJ
iajs-3728	105	21	numbers	number	NOUN
iajs-3728	105	22	of	of	ADP
iajs-3728	105	23	communities	community	NOUN
iajs-3728	105	24	and	and	CCONJ
iajs-3728	105	25	parameter	parameter	NOUN
iajs-3728	105	26	values	value	NOUN
iajs-3728	105	27	.	.	PUNCT
iajs-3728	106	1	the	the	DET
iajs-3728	106	2	akaike	akaike	ADJ
iajs-3728	106	3	information	information	NOUN
iajs-3728	106	4	criterion	criterion	NOUN
iajs-3728	106	5	(	(	PUNCT
iajs-3728	106	6	aic	aic	PROPN
iajs-3728	106	7	)	)	PUNCT
iajs-3728	106	8	and	and	CCONJ
iajs-3728	106	9	bayesian	bayesian	NOUN
iajs-3728	106	10	information	information	NOUN
iajs-3728	106	11	criterion	criterion	NOUN
iajs-3728	106	12	(	(	PUNCT
iajs-3728	106	13	bic	bic	PROPN
iajs-3728	106	14	)	)	PUNCT
iajs-3728	106	15	are	be	AUX
iajs-3728	106	16	commonly	commonly	ADV
iajs-3728	106	17	used	use	VERB
iajs-3728	106	18	to	to	PART
iajs-3728	106	19	compare	compare	VERB
iajs-3728	106	20	models	model	NOUN
iajs-3728	106	21	with	with	ADP
iajs-3728	106	22	different	different	ADJ
iajs-3728	106	23	numbers	number	NOUN
iajs-3728	106	24	of	of	ADP
iajs-3728	106	25	parameters	parameter	NOUN
iajs-3728	106	26	and	and	CCONJ
iajs-3728	106	27	select	select	VERB
iajs-3728	106	28	an	an	DET
iajs-3728	106	29	appropriate	appropriate	ADJ
iajs-3728	106	30	model	model	NOUN
iajs-3728	106	31	.	.	PUNCT
iajs-3728	107	1	let	let	VERB
iajs-3728	107	2	r	r	NOUN
iajs-3728	107	3	be	be	AUX
iajs-3728	107	4	the	the	DET
iajs-3728	107	5	number	number	NOUN
iajs-3728	107	6	of	of	ADP
iajs-3728	107	7	estimated	estimate	VERB
iajs-3728	107	8	parameters	parameter	NOUN
iajs-3728	107	9	in	in	ADP
iajs-3728	107	10	the	the	DET
iajs-3728	107	11	model	model	NOUN
iajs-3728	107	12	(	(	PUNCT
iajs-3728	107	13	)	)	PUNCT
iajs-3728	107	14	(	(	PUNCT
iajs-3728	107	15	14	14	NUM
iajs-3728	107	16	)	)	PUNCT
iajs-3728	107	17	and	and	CCONJ
iajs-3728	107	18	(	(	PUNCT
iajs-3728	107	19	)	)	PUNCT
iajs-3728	107	20	(	(	PUNCT
iajs-3728	107	21	)	)	PUNCT
iajs-3728	107	22	(	(	PUNCT
iajs-3728	107	23	15	15	NUM
iajs-3728	107	24	)	)	PUNCT
iajs-3728	107	25	the	the	DET
iajs-3728	107	26	model	model	NOUN
iajs-3728	107	27	that	that	PRON
iajs-3728	107	28	has	have	VERB
iajs-3728	107	29	the	the	DET
iajs-3728	107	30	lowest	low	ADJ
iajs-3728	107	31	aic	aic	PROPN
iajs-3728	107	32	and	and	CCONJ
iajs-3728	107	33	bic	bic	PROPN
iajs-3728	107	34	values	value	NOUN
iajs-3728	107	35	from	from	ADP
iajs-3728	107	36	a	a	DET
iajs-3728	107	37	group	group	NOUN
iajs-3728	107	38	of	of	ADP
iajs-3728	107	39	possible	possible	ADJ
iajs-3728	107	40	models	model	NOUN
iajs-3728	107	41	for	for	ADP
iajs-3728	107	42	the	the	DET
iajs-3728	107	43	data	data	NOUN
iajs-3728	107	44	is	be	AUX
iajs-3728	107	45	the	the	DET
iajs-3728	107	46	one	one	NOUN
iajs-3728	107	47	that	that	PRON
iajs-3728	107	48	should	should	AUX
iajs-3728	107	49	be	be	AUX
iajs-3728	107	50	selected	select	VERB
iajs-3728	107	51	.	.	PUNCT
iajs-3728	108	1	in	in	ADP
iajs-3728	108	2	other	other	ADJ
iajs-3728	108	3	words	word	NOUN
iajs-3728	108	4	,	,	PUNCT
iajs-3728	108	5	the	the	DET
iajs-3728	108	6	aic	aic	PROPN
iajs-3728	108	7	or	or	CCONJ
iajs-3728	108	8	bic	bic	PROPN
iajs-3728	108	9	can	can	AUX
iajs-3728	108	10	be	be	AUX
iajs-3728	108	11	used	use	VERB
iajs-3728	108	12	to	to	PART
iajs-3728	108	13	compare	compare	VERB
iajs-3728	108	14	different	different	ADJ
iajs-3728	108	15	models	model	NOUN
iajs-3728	108	16	with	with	ADP
iajs-3728	108	17	different	different	ADJ
iajs-3728	108	18	numbers	number	NOUN
iajs-3728	108	19	of	of	ADP
iajs-3728	108	20	parameters	parameter	NOUN
iajs-3728	108	21	and	and	CCONJ
iajs-3728	108	22	select	select	VERB
iajs-3728	108	23	the	the	DET
iajs-3728	108	24	one	one	NOUN
iajs-3728	108	25	that	that	PRON
iajs-3728	108	26	best	well	ADV
iajs-3728	108	27	fits	fit	VERB
iajs-3728	108	28	the	the	DET
iajs-3728	108	29	data	datum	NOUN
iajs-3728	108	30	,	,	PUNCT
iajs-3728	108	31	where	where	SCONJ
iajs-3728	108	32	the	the	DET
iajs-3728	108	33	lower	low	ADJ
iajs-3728	108	34	the	the	DET
iajs-3728	108	35	aic	aic	PROPN
iajs-3728	108	36	or	or	CCONJ
iajs-3728	108	37	bic	bic	PROPN
iajs-3728	108	38	value	value	NOUN
iajs-3728	108	39	,	,	PUNCT
iajs-3728	108	40	the	the	PRON
iajs-3728	108	41	better	well	ADJ
iajs-3728	108	42	the	the	DET
iajs-3728	108	43	model	model	NOUN
iajs-3728	108	44	fits	fit	VERB
iajs-3728	108	45	the	the	DET
iajs-3728	108	46	data	datum	NOUN
iajs-3728	108	47	(	(	PUNCT
iajs-3728	108	48	30	30	NUM
iajs-3728	108	49	)	)	PUNCT
iajs-3728	108	50	.	.	PUNCT
iajs-3728	109	1	therefore	therefore	ADV
iajs-3728	109	2	,	,	PUNCT
iajs-3728	109	3	two	two	NUM
iajs-3728	109	4	stages	stage	NOUN
iajs-3728	109	5	are	be	AUX
iajs-3728	109	6	needed	need	VERB
iajs-3728	109	7	to	to	PART
iajs-3728	109	8	estimate	estimate	VERB
iajs-3728	109	9	the	the	DET
iajs-3728	109	10	parameters	parameter	NOUN
iajs-3728	109	11	,	,	PUNCT
iajs-3728	109	12	.and	.and	PUNCT
iajs-3728	109	13	choose	choose	VERB
iajs-3728	109	14	the	the	DET
iajs-3728	109	15	optimum	optimum	ADJ
iajs-3728	109	16	model	model	NOUN
iajs-3728	109	17	.	.	PUNCT
iajs-3728	110	1	the	the	DET
iajs-3728	110	2	first	first	ADJ
iajs-3728	110	3	is	be	AUX
iajs-3728	110	4	to	to	PART
iajs-3728	110	5	choose	choose	VERB
iajs-3728	110	6	the	the	DET
iajs-3728	110	7	best	good	ADJ
iajs-3728	110	8	membership	membership	NOUN
iajs-3728	110	9	label	label	NOUN
iajs-3728	110	10	zbest	zbest	PROPN
iajs-3728	110	11	,	,	PUNCT
iajs-3728	110	12	which	which	PRON
iajs-3728	110	13	represents	represent	VERB
iajs-3728	110	14	the	the	DET
iajs-3728	110	15	partition	partition	NOUN
iajs-3728	110	16	of	of	ADP
iajs-3728	110	17	the	the	DET
iajs-3728	110	18	network	network	NOUN
iajs-3728	110	19	,	,	PUNCT
iajs-3728	110	20	using	use	VERB
iajs-3728	110	21	any	any	DET
iajs-3728	110	22	community	community	NOUN
iajs-3728	110	23	detection	detection	NOUN
iajs-3728	110	24	algorithm	algorithm	NOUN
iajs-3728	110	25	for	for	ADP
iajs-3728	110	26	many	many	ADJ
iajs-3728	110	27	values	value	NOUN
iajs-3728	110	28	k<<n	k<<n	VERB
iajs-3728	110	29	such	such	ADJ
iajs-3728	110	30	that	that	PRON
iajs-3728	110	31	,	,	PUNCT
iajs-3728	110	32	(	(	PUNCT
iajs-3728	110	33	|	|	ADV
iajs-3728	110	34	)	)	PUNCT
iajs-3728	110	35	.	.	PUNCT
iajs-3728	111	1	and	and	CCONJ
iajs-3728	111	2	find	find	VERB
iajs-3728	111	3	the	the	DET
iajs-3728	111	4	likelihood	likelihood	NOUN
iajs-3728	111	5	for	for	ADP
iajs-3728	111	6	every	every	DET
iajs-3728	111	7	partition	partition	NOUN
iajs-3728	111	8	using	use	VERB
iajs-3728	111	9	equation	equation	NOUN
iajs-3728	111	10	(	(	PUNCT
iajs-3728	111	11	7	7	NUM
iajs-3728	111	12	)	)	PUNCT
iajs-3728	111	13	or	or	CCONJ
iajs-3728	111	14	(	(	PUNCT
iajs-3728	111	15	8)	8)	NUM
iajs-3728	111	16	.	.	PUNCT
iajs-3728	112	1	this	this	DET
iajs-3728	112	2	probability	probability	NOUN
iajs-3728	112	3	indicates	indicate	VERB
iajs-3728	112	4	how	how	SCONJ
iajs-3728	112	5	likely	likely	ADJ
iajs-3728	112	6	it	it	PRON
iajs-3728	112	7	is	be	AUX
iajs-3728	112	8	that	that	SCONJ
iajs-3728	112	9	the	the	DET
iajs-3728	112	10	observed	observe	VERB
iajs-3728	112	11	data	datum	NOUN
iajs-3728	112	12	a	a	DET
iajs-3728	112	13	matches	match	NOUN
iajs-3728	112	14	the	the	DET
iajs-3728	112	15	model	model	NOUN
iajs-3728	112	16	specified	specify	VERB
iajs-3728	112	17	by	by	ADP
iajs-3728	112	18	the	the	DET
iajs-3728	112	19	optimized	optimize	VERB
iajs-3728	112	20	parameters	parameter	NOUN
iajs-3728	112	21	b	b	PROPN
iajs-3728	112	22	for	for	ADP
iajs-3728	112	23	the	the	DET
iajs-3728	112	24	best	good	ADJ
iajs-3728	112	25	label	label	NOUN
iajs-3728	112	26	z.	z.	PROPN
iajs-3728	112	27	in	in	ADP
iajs-3728	112	28	the	the	DET
iajs-3728	112	29	second	second	ADJ
iajs-3728	112	30	phase	phase	NOUN
iajs-3728	112	31	,	,	PUNCT
iajs-3728	112	32	the	the	DET
iajs-3728	112	33	corresponding	correspond	VERB
iajs-3728	112	34	aic	aic	PROPN
iajs-3728	112	35	or	or	CCONJ
iajs-3728	112	36	bic	bic	PROPN
iajs-3728	112	37	values	value	NOUN
iajs-3728	112	38	are	be	AUX
iajs-3728	112	39	determined	determine	VERB
iajs-3728	112	40	,	,	PUNCT
iajs-3728	112	41	equations	equation	NOUN
iajs-3728	112	42	(	(	PUNCT
iajs-3728	112	43	6	6	NUM
iajs-3728	112	44	)	)	PUNCT
iajs-3728	112	45	and	and	CCONJ
iajs-3728	112	46	(	(	PUNCT
iajs-3728	112	47	7	7	X
iajs-3728	112	48	)	)	PUNCT
iajs-3728	112	49	respectively	respectively	ADV
iajs-3728	112	50	.	.	PUNCT
iajs-3728	113	1	as	as	ADP
iajs-3728	113	2	a	a	DET
iajs-3728	113	3	result	result	NOUN
iajs-3728	113	4	,	,	PUNCT
iajs-3728	113	5	the	the	DET
iajs-3728	113	6	model	model	NOUN
iajs-3728	113	7	that	that	PRON
iajs-3728	113	8	best	well	ADV
iajs-3728	113	9	fits	fit	VERB
iajs-3728	113	10	the	the	DET
iajs-3728	113	11	data	datum	NOUN
iajs-3728	113	12	and	and	CCONJ
iajs-3728	113	13	has	have	VERB
iajs-3728	113	14	a	a	PRON
iajs-3728	113	15	lower	low	ADJ
iajs-3728	113	16	aic	aic	PROPN
iajs-3728	113	17	or	or	CCONJ
iajs-3728	113	18	bic	bic	PROPN
iajs-3728	113	19	value	value	NOUN
iajs-3728	113	20	is	be	AUX
iajs-3728	113	21	selected	select	VERB
iajs-3728	113	22	.	.	PUNCT
iajs-3728	114	1	this	this	DET
iajs-3728	114	2	procedure	procedure	NOUN
iajs-3728	114	3	can	can	AUX
iajs-3728	114	4	be	be	AUX
iajs-3728	114	5	summarized	summarize	VERB
iajs-3728	114	6	using	use	VERB
iajs-3728	114	7	the	the	DET
iajs-3728	114	8	flowchart	flowchart	NOUN
iajs-3728	114	9	in	in	ADP
iajs-3728	114	10	figure	figure	NOUN
iajs-3728	114	11	(	(	PUNCT
iajs-3728	114	12	1	1	NUM
iajs-3728	114	13	)	)	PUNCT
iajs-3728	114	14	with	with	ADP
iajs-3728	114	15	its	its	PRON
iajs-3728	114	16	algorithm	algorithm	NOUN
iajs-3728	114	17	(	(	PUNCT
iajs-3728	114	18	1	1	NUM
iajs-3728	114	19	)	)	PUNCT
iajs-3728	114	20	.	.	PUNCT
iajs-3728	115	1	algorithm	algorithm	NOUN
iajs-3728	115	2	(	(	PUNCT
iajs-3728	115	3	1	1	X
iajs-3728	115	4	)	)	PUNCT
iajs-3728	115	5	can	can	AUX
iajs-3728	115	6	be	be	AUX
iajs-3728	115	7	used	use	VERB
iajs-3728	115	8	with	with	ADP
iajs-3728	115	9	any	any	DET
iajs-3728	115	10	community	community	NOUN
iajs-3728	115	11	detection	detection	NOUN
iajs-3728	115	12	algorithm	algorithm	NOUN
iajs-3728	115	13	of	of	ADP
iajs-3728	115	14	a	a	DET
iajs-3728	115	15	network	network	NOUN
iajs-3728	115	16	with	with	ADP
iajs-3728	115	17	n	n	PRON
iajs-3728	115	18	nodes	node	NOUN
iajs-3728	115	19	for	for	ADP
iajs-3728	115	20	any	any	DET
iajs-3728	115	21	chosen	choose	VERB
iajs-3728	115	22	number	number	NOUN
iajs-3728	115	23	of	of	ADP
iajs-3728	115	24	communities	community	NOUN
iajs-3728	115	25	k	k	PROPN
iajs-3728	115	26	to	to	PART
iajs-3728	115	27	identify	identify	VERB
iajs-3728	115	28	the	the	DET
iajs-3728	115	29	model	model	NOUN
iajs-3728	115	30	that	that	PRON
iajs-3728	115	31	is	be	AUX
iajs-3728	115	32	more	more	ADV
iajs-3728	115	33	likely	likely	ADJ
iajs-3728	115	34	to	to	PART
iajs-3728	115	35	fit	fit	VERB
iajs-3728	115	36	the	the	DET
iajs-3728	115	37	observed	observed	ADJ
iajs-3728	115	38	network	network	NOUN
iajs-3728	115	39	or	or	CCONJ
iajs-3728	115	40	discover	discover	VERB
iajs-3728	115	41	k	k	PROPN
iajs-3728	115	42	for	for	ADP
iajs-3728	115	43	a	a	DET
iajs-3728	115	44	model	model	NOUN
iajs-3728	115	45	that	that	PRON
iajs-3728	115	46	is	be	AUX
iajs-3728	115	47	more	more	ADV
iajs-3728	115	48	likely	likely	ADJ
iajs-3728	115	49	to	to	PART
iajs-3728	115	50	do	do	VERB
iajs-3728	115	51	so	so	ADV
iajs-3728	115	52	.	.	PUNCT
iajs-3728	116	1	ihjpas	ihjpas	PROPN
iajs-3728	116	2	.	.	PUNCT
iajs-3728	117	1	2025	2025	NUM
iajs-3728	117	2	,	,	PUNCT
iajs-3728	117	3	38(3	38(3	NUM
iajs-3728	117	4	)	)	PUNCT
iajs-3728	117	5	331	331	NUM
iajs-3728	117	6	figure1	figure1	NUM
iajs-3728	117	7	:	:	PUNCT
iajs-3728	117	8	flowchart	flowchart	NOUN
iajs-3728	117	9	for	for	ADP
iajs-3728	117	10	model	model	NOUN
iajs-3728	117	11	selection	selection	NOUN
iajs-3728	117	12	.	.	PUNCT
iajs-3728	118	1	algorithm	algorithm	PROPN
iajs-3728	118	2	1	1	NUM
iajs-3728	118	3	.	.	PUNCT
iajs-3728	118	4	model	model	NOUN
iajs-3728	118	5	selection	selection	NOUN
iajs-3728	118	6	algorithm	algorithm	NOUN
iajs-3728	118	7	for	for	ADP
iajs-3728	118	8	sbms	sbms	PROPN
iajs-3728	118	9	step1	step1	PROPN
iajs-3728	118	10	:	:	PUNCT
iajs-3728	118	11	input	input	NOUN
iajs-3728	118	12	:	:	PUNCT
iajs-3728	118	13	network	network	NOUN
iajs-3728	118	14	adjacency	adjacency	NOUN
iajs-3728	118	15	matrix	matrix	NOUN
iajs-3728	118	16	a	a	PRON
iajs-3728	118	17	of	of	ADP
iajs-3728	118	18	n	n	PRON
iajs-3728	118	19	by	by	ADP
iajs-3728	118	20	n	n	PRON
iajs-3728	118	21	elements	element	NOUN
iajs-3728	118	22	.	.	PUNCT
iajs-3728	119	1	step	step	NOUN
iajs-3728	119	2	2	2	NUM
iajs-3728	119	3	:	:	PUNCT
iajs-3728	119	4	for	for	ADP
iajs-3728	119	5	k=2	k=2	PROPN
iajs-3728	119	6	to	to	ADP
iajs-3728	119	7	n	n	PROPN
iajs-3728	119	8	,	,	PUNCT
iajs-3728	119	9	where	where	SCONJ
iajs-3728	119	10	n<<n	n<<n	PROPN
iajs-3728	119	11	find	find	VERB
iajs-3728	119	12	the	the	DET
iajs-3728	119	13	label	label	NOUN
iajs-3728	119	14	vector	vector	NOUN
iajs-3728	119	15	z	z	NOUN
iajs-3728	119	16	by	by	ADP
iajs-3728	119	17	using	use	VERB
iajs-3728	119	18	any	any	DET
iajs-3728	119	19	community	community	NOUN
iajs-3728	119	20	detection	detection	NOUN
iajs-3728	119	21	algorithm	algorithm	NOUN
iajs-3728	119	22	.	.	PUNCT
iajs-3728	120	1	find	find	VERB
iajs-3728	120	2	its	its	PRON
iajs-3728	120	3	likelihood	likelihood	NOUN
iajs-3728	120	4	using	use	VERB
iajs-3728	120	5	equation	equation	NOUN
iajs-3728	120	6	(	(	PUNCT
iajs-3728	120	7	7	7	NUM
iajs-3728	120	8	)	)	PUNCT
iajs-3728	120	9	or	or	CCONJ
iajs-3728	120	10	(	(	PUNCT
iajs-3728	120	11	8)	8)	NUM
iajs-3728	120	12	.	.	PUNCT
iajs-3728	121	1	step3	step3	PROPN
iajs-3728	121	2	:	:	PUNCT
iajs-3728	121	3	put	put	VERB
iajs-3728	121	4	the	the	DET
iajs-3728	121	5	likelihood	likelihood	NOUN
iajs-3728	121	6	values	value	NOUN
iajs-3728	121	7	in	in	ADP
iajs-3728	121	8	statistical	statistical	ADJ
iajs-3728	121	9	model	model	NOUN
iajs-3728	121	10	selection	selection	NOUN
iajs-3728	121	11	criteria	criterion	NOUN
iajs-3728	121	12	aic	aic	PROPN
iajs-3728	121	13	or	or	CCONJ
iajs-3728	121	14	bic	bic	PROPN
iajs-3728	121	15	equation	equation	NOUN
iajs-3728	121	16	(	(	PUNCT
iajs-3728	121	17	14	14	NUM
iajs-3728	121	18	)	)	PUNCT
iajs-3728	121	19	or	or	CCONJ
iajs-3728	121	20	(	(	PUNCT
iajs-3728	121	21	15	15	NUM
iajs-3728	121	22	)	)	PUNCT
iajs-3728	121	23	.	.	PUNCT
iajs-3728	122	1	step4	step4	PROPN
iajs-3728	122	2	:	:	PUNCT
iajs-3728	122	3	pick	pick	VERB
iajs-3728	122	4	up	up	ADP
iajs-3728	122	5	the	the	DET
iajs-3728	122	6	model	model	NOUN
iajs-3728	122	7	with	with	ADP
iajs-3728	122	8	smallest	small	ADJ
iajs-3728	122	9	aic	aic	PROPN
iajs-3728	122	10	or	or	CCONJ
iajs-3728	122	11	bic	bic	PROPN
iajs-3728	122	12	.	.	PUNCT
iajs-3728	123	1	step5	step5	PROPN
iajs-3728	123	2	:	:	PUNCT
iajs-3728	123	3	output	output	NOUN
iajs-3728	123	4	:	:	PUNCT
iajs-3728	123	5	the	the	DET
iajs-3728	123	6	selected	select	VERB
iajs-3728	123	7	model	model	NOUN
iajs-3728	123	8	with	with	ADP
iajs-3728	123	9	communities	community	NOUN
iajs-3728	123	10	k	k	PROPN
iajs-3728	123	11	and	and	CCONJ
iajs-3728	123	12	its	its	PRON
iajs-3728	123	13	associated	associated	ADJ
iajs-3728	123	14	likelihood	likelihood	NOUN
iajs-3728	123	15	for	for	ADP
iajs-3728	123	16	given	give	VERB
iajs-3728	123	17	z.	z.	PROPN
iajs-3728	123	18	5.empirical	5.empirical	NUM
iajs-3728	123	19	example	example	NOUN
iajs-3728	123	20	and	and	CCONJ
iajs-3728	123	21	results	result	VERB
iajs-3728	123	22	algorithm	algorithm	NOUN
iajs-3728	123	23	(	(	PUNCT
iajs-3728	123	24	1	1	X
iajs-3728	123	25	)	)	PUNCT
iajs-3728	123	26	was	be	AUX
iajs-3728	123	27	implemented	implement	VERB
iajs-3728	123	28	on	on	ADP
iajs-3728	123	29	various	various	ADJ
iajs-3728	123	30	real	real	ADJ
iajs-3728	123	31	and	and	CCONJ
iajs-3728	123	32	synthetic	synthetic	ADJ
iajs-3728	123	33	networks	network	NOUN
iajs-3728	123	34	using	use	VERB
iajs-3728	123	35	a	a	DET
iajs-3728	123	36	dataset	dataset	NOUN
iajs-3728	123	37	from	from	ADP
iajs-3728	123	38	the	the	DET
iajs-3728	123	39	stanford	stanford	PROPN
iajs-3728	123	40	network	network	PROPN
iajs-3728	123	41	analysis	analysis	NOUN
iajs-3728	123	42	platform	platform	NOUN
iajs-3728	123	43	(	(	PUNCT
iajs-3728	123	44	snap	snap	NOUN
iajs-3728	123	45	)	)	PUNCT
iajs-3728	123	46	network	network	NOUN
iajs-3728	123	47	analysis	analysis	NOUN
iajs-3728	123	48	library	library	NOUN
iajs-3728	123	49	.	.	PUNCT
iajs-3728	124	1	table	table	NOUN
iajs-3728	124	2	(	(	PUNCT
iajs-3728	124	3	1	1	X
iajs-3728	124	4	)	)	PUNCT
iajs-3728	124	5	shows	show	VERB
iajs-3728	124	6	the	the	DET
iajs-3728	124	7	total	total	ADJ
iajs-3728	124	8	number	number	NOUN
iajs-3728	124	9	of	of	ADP
iajs-3728	124	10	nodes	node	NOUN
iajs-3728	124	11	and	and	CCONJ
iajs-3728	124	12	edges	edge	NOUN
iajs-3728	124	13	in	in	ADP
iajs-3728	124	14	each	each	DET
iajs-3728	124	15	network	network	NOUN
iajs-3728	124	16	analyzed	analyze	VERB
iajs-3728	124	17	.	.	PUNCT
iajs-3728	125	1	table	table	NOUN
iajs-3728	125	2	1	1	NUM
iajs-3728	125	3	.	.	PUNCT
iajs-3728	126	1	datasets	dataset	NOUN
iajs-3728	126	2	for	for	ADP
iajs-3728	126	3	various	various	ADJ
iajs-3728	126	4	networks	network	NOUN
iajs-3728	126	5	from	from	ADP
iajs-3728	126	6	stanford	stanford	PROPN
iajs-3728	126	7	large	large	ADJ
iajs-3728	126	8	network	network	NOUN
iajs-3728	126	9	dataset	dataset	VERB
iajs-3728	126	10	collection	collection	NOUN
iajs-3728	126	11	no	no	PROPN
iajs-3728	126	12	.	.	PUNCT
iajs-3728	127	1	network	network	NOUN
iajs-3728	127	2	vertices	vertice	VERB
iajs-3728	127	3	no	no	INTJ
iajs-3728	127	4	.	.	PUNCT
iajs-3728	128	1	edges	edge	VERB
iajs-3728	128	2	no	no	INTJ
iajs-3728	128	3	.	.	NOUN
iajs-3728	128	4	1	1	NUM
iajs-3728	128	5	dolphin	dolphin	NOUN
iajs-3728	128	6	62	62	NUM
iajs-3728	128	7	159	159	NUM
iajs-3728	128	8	2	2	NUM
iajs-3728	128	9	karate	karate	NOUN
iajs-3728	128	10	34	34	NUM
iajs-3728	128	11	78	78	NUM
iajs-3728	128	12	3	3	NUM
iajs-3728	128	13	american	american	PROPN
iajs-3728	128	14	football	football	PROPN
iajs-3728	128	15	college	college	PROPN
iajs-3728	128	16	(	(	PUNCT
iajs-3728	128	17	afc	afc	PROPN
iajs-3728	128	18	)	)	PUNCT
iajs-3728	128	19	115	115	NUM
iajs-3728	128	20	613	613	NUM
iajs-3728	128	21	6	6	NUM
iajs-3728	128	22	chesapeake	chesapeake	NOUN
iajs-3728	128	23	synthetic	synthetic	NOUN
iajs-3728	128	24	39	39	NUM
iajs-3728	128	25	170	170	NUM
iajs-3728	128	26	7	7	NUM
iajs-3728	128	27	delaunay	delaunay	PROPN
iajs-3728	128	28	synthetic	synthetic	PROPN
iajs-3728	128	29	1024	1024	NUM
iajs-3728	128	30	3056	3056	NUM
iajs-3728	128	31	as	as	ADP
iajs-3728	128	32	an	an	DET
iajs-3728	128	33	example	example	NOUN
iajs-3728	128	34	,	,	PUNCT
iajs-3728	128	35	the	the	DET
iajs-3728	128	36	experiment	experiment	NOUN
iajs-3728	128	37	is	be	AUX
iajs-3728	128	38	performed	perform	VERB
iajs-3728	128	39	with	with	ADP
iajs-3728	128	40	a	a	DET
iajs-3728	128	41	dolphin	dolphin	NOUN
iajs-3728	128	42	network	network	NOUN
iajs-3728	128	43	(	(	PUNCT
iajs-3728	128	44	62	62	NUM
iajs-3728	128	45	nodes	node	NOUN
iajs-3728	128	46	and	and	CCONJ
iajs-3728	128	47	159	159	NUM
iajs-3728	128	48	edges	edge	NOUN
iajs-3728	128	49	)	)	PUNCT
iajs-3728	128	50	,	,	PUNCT
iajs-3728	128	51	as	as	SCONJ
iajs-3728	128	52	shown	show	VERB
iajs-3728	128	53	in	in	ADP
iajs-3728	128	54	table	table	NOUN
iajs-3728	128	55	(	(	PUNCT
iajs-3728	128	56	1	1	NUM
iajs-3728	128	57	)	)	PUNCT
iajs-3728	128	58	.	.	PUNCT
iajs-3728	129	1	first	first	ADV
iajs-3728	129	2	,	,	PUNCT
iajs-3728	129	3	the	the	DET
iajs-3728	129	4	detection	detection	NOUN
iajs-3728	129	5	is	be	AUX
iajs-3728	129	6	performed	perform	VERB
iajs-3728	129	7	at	at	ADP
iajs-3728	129	8	k=4	k=4	PROPN
iajs-3728	129	9	with	with	ADP
iajs-3728	129	10	many	many	ADJ
iajs-3728	129	11	algorithms	algorithm	NOUN
iajs-3728	129	12	including	include	VERB
iajs-3728	129	13	rcmm	rcmm	PROPN
iajs-3728	129	14	,	,	PUNCT
iajs-3728	129	15	cmm	cmm	NOUN
iajs-3728	129	16	,	,	PUNCT
iajs-3728	129	17	danon	danon	ADJ
iajs-3728	129	18	method	method	NOUN
iajs-3728	129	19	,	,	PUNCT
iajs-3728	129	20	newman	newman	PROPN
iajs-3728	129	21	modularity	modularity	PROPN
iajs-3728	129	22	maximization	maximization	PROPN
iajs-3728	129	23	,	,	PUNCT
iajs-3728	129	24	and	and	CCONJ
iajs-3728	129	25	spectral	spectral	ADJ
iajs-3728	129	26	clustering	clustering	NOUN
iajs-3728	129	27	with	with	ADP
iajs-3728	129	28	the	the	DET
iajs-3728	129	29	corresponding	corresponding	ADJ
iajs-3728	129	30	likelihood	likelihood	NOUN
iajs-3728	129	31	values	value	NOUN
iajs-3728	129	32	shown	show	VERB
iajs-3728	129	33	in	in	ADP
iajs-3728	129	34	figure	figure	NOUN
iajs-3728	129	35	(	(	PUNCT
iajs-3728	129	36	2	2	NUM
iajs-3728	129	37	)	)	PUNCT
iajs-3728	129	38	.	.	PUNCT
iajs-3728	130	1	then	then	ADV
iajs-3728	130	2	,	,	PUNCT
iajs-3728	130	3	as	as	SCONJ
iajs-3728	130	4	mentioned	mention	VERB
iajs-3728	130	5	ihjpas	ihjpa	NOUN
iajs-3728	130	6	.	.	PUNCT
iajs-3728	131	1	2025	2025	NUM
iajs-3728	131	2	,	,	PUNCT
iajs-3728	131	3	38(3	38(3	NUM
iajs-3728	131	4	)	)	PUNCT
iajs-3728	131	5	332	332	NUM
iajs-3728	131	6	earlier	early	ADV
iajs-3728	131	7	,	,	PUNCT
iajs-3728	131	8	the	the	DET
iajs-3728	131	9	best	good	ADJ
iajs-3728	131	10	label	label	NOUN
iajs-3728	131	11	vector	vector	NOUN
iajs-3728	131	12	corresponding	correspond	VERB
iajs-3728	131	13	to	to	ADP
iajs-3728	131	14	the	the	DET
iajs-3728	131	15	minimum	minimum	ADJ
iajs-3728	131	16	likelihood	likelihood	NOUN
iajs-3728	131	17	value	value	NOUN
iajs-3728	131	18	is	be	AUX
iajs-3728	131	19	chosen	choose	VERB
iajs-3728	131	20	.	.	PUNCT
iajs-3728	132	1	all	all	DET
iajs-3728	132	2	calculations	calculation	NOUN
iajs-3728	132	3	are	be	AUX
iajs-3728	132	4	performed	perform	VERB
iajs-3728	132	5	with	with	ADP
iajs-3728	132	6	a	a	DET
iajs-3728	132	7	degree	degree	NOUN
iajs-3728	132	8	-	-	PUNCT
iajs-3728	132	9	corrected	correct	VERB
iajs-3728	132	10	model	model	NOUN
iajs-3728	132	11	,	,	PUNCT
iajs-3728	132	12	as	as	SCONJ
iajs-3728	132	13	it	it	PRON
iajs-3728	132	14	is	be	AUX
iajs-3728	132	15	more	more	ADV
iajs-3728	132	16	suitable	suitable	ADJ
iajs-3728	132	17	for	for	ADP
iajs-3728	132	18	modeling	model	VERB
iajs-3728	132	19	networks	network	NOUN
iajs-3728	132	20	.	.	PUNCT
iajs-3728	133	1	log	log	VERB
iajs-3728	133	2	-	-	PUNCT
iajs-3728	133	3	likelihood=-1.6117e+03	likelihood=-1.6117e+03	NOUN
iajs-3728	133	4	log	log	NOUN
iajs-3728	133	5	-	-	PUNCT
iajs-3728	133	6	likelihood=-1.6118e+03	likelihood=-1.6118e+03	NOUN
iajs-3728	133	7	log	log	NOUN
iajs-3728	133	8	-	-	PUNCT
iajs-3728	133	9	likelihood=-1.6218e+03	likelihood=-1.6218e+03	NOUN
iajs-3728	133	10	log	log	NOUN
iajs-3728	133	11	-	-	PUNCT
iajs-3728	133	12	likelihood=-1.6386e+03	likelihood=-1.6386e+03	NOUN
iajs-3728	133	13	figure	figure	NOUN
iajs-3728	133	14	2	2	NUM
iajs-3728	133	15	.	.	PUNCT
iajs-3728	133	16	dolphin	dolphin	NOUN
iajs-3728	133	17	network	network	NOUN
iajs-3728	133	18	partition	partition	NOUN
iajs-3728	133	19	for	for	ADP
iajs-3728	133	20	k=4	k=4	PROPN
iajs-3728	133	21	,	,	PUNCT
iajs-3728	133	22	using	use	VERB
iajs-3728	133	23	different	different	ADJ
iajs-3728	133	24	community	community	NOUN
iajs-3728	133	25	detection	detection	NOUN
iajs-3728	133	26	algorithms	algorithm	NOUN
iajs-3728	133	27	under	under	ADP
iajs-3728	133	28	degree	degree	NOUN
iajs-3728	133	29	corrected	correct	VERB
iajs-3728	133	30	model	model	NOUN
iajs-3728	133	31	with	with	ADP
iajs-3728	133	32	each	each	DET
iajs-3728	133	33	log	log	NOUN
iajs-3728	133	34	-	-	PUNCT
iajs-3728	133	35	likelihood	likelihood	NOUN
iajs-3728	133	36	values	value	NOUN
iajs-3728	133	37	.	.	PUNCT
iajs-3728	134	1	for	for	ADP
iajs-3728	134	2	model	model	NOUN
iajs-3728	134	3	selection	selection	NOUN
iajs-3728	134	4	,	,	PUNCT
iajs-3728	134	5	the	the	DET
iajs-3728	134	6	above	above	ADJ
iajs-3728	134	7	experiment	experiment	NOUN
iajs-3728	134	8	is	be	AUX
iajs-3728	134	9	applied	apply	VERB
iajs-3728	134	10	for	for	ADP
iajs-3728	134	11	many	many	ADJ
iajs-3728	134	12	k	k	PROPN
iajs-3728	134	13	and	and	CCONJ
iajs-3728	134	14	on	on	ADP
iajs-3728	134	15	each	each	DET
iajs-3728	134	16	network	network	NOUN
iajs-3728	134	17	with	with	ADP
iajs-3728	134	18	the	the	DET
iajs-3728	134	19	same	same	ADJ
iajs-3728	134	20	community	community	NOUN
iajs-3728	134	21	detection	detection	NOUN
iajs-3728	134	22	algorithm	algorithm	NOUN
iajs-3728	134	23	(	(	PUNCT
iajs-3728	134	24	see	see	VERB
iajs-3728	134	25	algorithm	algorithm	NOUN
iajs-3728	134	26	1	1	NUM
iajs-3728	134	27	)	)	PUNCT
iajs-3728	134	28	,	,	PUNCT
iajs-3728	134	29	selecting	select	VERB
iajs-3728	134	30	the	the	DET
iajs-3728	134	31	model	model	NOUN
iajs-3728	134	32	that	that	PRON
iajs-3728	134	33	has	have	VERB
iajs-3728	134	34	the	the	DET
iajs-3728	134	35	lower	low	ADJ
iajs-3728	134	36	likelihood	likelihood	NOUN
iajs-3728	134	37	criteria	criterion	NOUN
iajs-3728	134	38	for	for	ADP
iajs-3728	134	39	model	model	NOUN
iajs-3728	134	40	selection	selection	NOUN
iajs-3728	134	41	,	,	PUNCT
iajs-3728	134	42	and	and	CCONJ
iajs-3728	134	43	here	here	ADV
iajs-3728	134	44	bic	bic	PROPN
iajs-3728	134	45	is	be	AUX
iajs-3728	134	46	chosen	choose	VERB
iajs-3728	134	47	.	.	PUNCT
iajs-3728	135	1	the	the	DET
iajs-3728	135	2	applied	apply	VERB
iajs-3728	135	3	community	community	NOUN
iajs-3728	135	4	detection	detection	NOUN
iajs-3728	135	5	algorithm	algorithm	NOUN
iajs-3728	135	6	is	be	AUX
iajs-3728	135	7	rcmm	rcmm	NOUN
iajs-3728	135	8	to	to	PART
iajs-3728	135	9	test	test	VERB
iajs-3728	135	10	whether	whether	SCONJ
iajs-3728	135	11	it	it	PRON
iajs-3728	135	12	is	be	AUX
iajs-3728	135	13	sufficient	sufficient	ADJ
iajs-3728	135	14	to	to	PART
iajs-3728	135	15	detect	detect	VERB
iajs-3728	135	16	the	the	DET
iajs-3728	135	17	correct	correct	ADJ
iajs-3728	135	18	model	model	NOUN
iajs-3728	135	19	(	(	PUNCT
iajs-3728	135	20	figure	figure	NOUN
iajs-3728	135	21	3	3	NUM
iajs-3728	135	22	)	)	PUNCT
iajs-3728	135	23	.	.	PUNCT
iajs-3728	136	1	these	these	DET
iajs-3728	136	2	results	result	NOUN
iajs-3728	136	3	are	be	AUX
iajs-3728	136	4	compared	compare	VERB
iajs-3728	136	5	with	with	ADP
iajs-3728	136	6	the	the	DET
iajs-3728	136	7	results	result	NOUN
iajs-3728	136	8	of	of	ADP
iajs-3728	136	9	other	other	ADJ
iajs-3728	136	10	community	community	NOUN
iajs-3728	136	11	detection	detection	NOUN
iajs-3728	136	12	methods	method	NOUN
iajs-3728	136	13	,	,	PUNCT
iajs-3728	136	14	namely	namely	ADV
iajs-3728	136	15	:	:	PUNCT
iajs-3728	136	16	danon	danon	ADJ
iajs-3728	136	17	algorithm	algorithm	NOUN
iajs-3728	136	18	,	,	PUNCT
iajs-3728	136	19	cmm	cmm	PROPN
iajs-3728	136	20	algorithm	algorithm	PROPN
iajs-3728	136	21	,	,	PUNCT
iajs-3728	136	22	modularity	modularity	NOUN
iajs-3728	136	23	maximization	maximization	NOUN
iajs-3728	136	24	algorithm	algorithm	NOUN
iajs-3728	136	25	and	and	CCONJ
iajs-3728	136	26	spectral	spectral	ADJ
iajs-3728	136	27	clustering	clustering	NOUN
iajs-3728	136	28	for	for	ADP
iajs-3728	136	29	many	many	ADJ
iajs-3728	136	30	networks	network	NOUN
iajs-3728	136	31	(	(	PUNCT
iajs-3728	136	32	see	see	VERB
iajs-3728	136	33	table	table	NOUN
iajs-3728	136	34	2	2	NUM
iajs-3728	136	35	)	)	PUNCT
iajs-3728	136	36	.	.	PUNCT
iajs-3728	137	1	in	in	ADP
iajs-3728	137	2	table	table	NOUN
iajs-3728	137	3	(	(	PUNCT
iajs-3728	137	4	2	2	NUM
iajs-3728	137	5	)	)	PUNCT
iajs-3728	137	6	,	,	PUNCT
iajs-3728	137	7	the	the	DET
iajs-3728	137	8	comparison	comparison	NOUN
iajs-3728	137	9	is	be	AUX
iajs-3728	137	10	done	do	VERB
iajs-3728	137	11	for	for	ADP
iajs-3728	137	12	model	model	NOUN
iajs-3728	137	13	selection	selection	NOUN
iajs-3728	137	14	between	between	ADP
iajs-3728	137	15	the	the	DET
iajs-3728	137	16	suggested	suggest	VERB
iajs-3728	137	17	method	method	NOUN
iajs-3728	137	18	rcmm	rcmm	PROPN
iajs-3728	137	19	and	and	CCONJ
iajs-3728	137	20	the	the	DET
iajs-3728	137	21	other	other	ADJ
iajs-3728	137	22	methods	method	NOUN
iajs-3728	137	23	,	,	PUNCT
iajs-3728	137	24	including	include	VERB
iajs-3728	137	25	:	:	PUNCT
iajs-3728	137	26	newman	newman	PROPN
iajs-3728	137	27	modularity	modularity	PROPN
iajs-3728	137	28	maximization	maximization	NOUN
iajs-3728	137	29	method	method	NOUN
iajs-3728	137	30	,	,	PUNCT
iajs-3728	137	31	danon	danon	ADJ
iajs-3728	137	32	method	method	NOUN
iajs-3728	137	33	,	,	PUNCT
iajs-3728	137	34	cmm	cmm	NOUN
iajs-3728	137	35	algorithm	algorithm	PROPN
iajs-3728	137	36	,	,	PUNCT
iajs-3728	137	37	and	and	CCONJ
iajs-3728	137	38	spectral	spectral	ADJ
iajs-3728	137	39	clustering	clustering	NOUN
iajs-3728	137	40	.	.	PUNCT
iajs-3728	138	1	ihjpas	ihjpas	PROPN
iajs-3728	138	2	.	.	PUNCT
iajs-3728	139	1	2025	2025	NUM
iajs-3728	139	2	,	,	PUNCT
iajs-3728	139	3	38(3	38(3	NUM
iajs-3728	139	4	)	)	PUNCT
iajs-3728	139	5	333	333	NUM
iajs-3728	139	6	a	a	DET
iajs-3728	139	7	b	b	NOUN
iajs-3728	139	8	c	c	NOUN
iajs-3728	139	9	d	d	NOUN
iajs-3728	139	10	figure	figure	NOUN
iajs-3728	139	11	3	3	NUM
iajs-3728	139	12	.	.	PUNCT
iajs-3728	139	13	model	model	NOUN
iajs-3728	139	14	selection	selection	NOUN
iajs-3728	139	15	for	for	ADP
iajs-3728	139	16	rcmm	rcmm	NOUN
iajs-3728	139	17	community	community	NOUN
iajs-3728	139	18	detection	detection	NOUN
iajs-3728	139	19	method	method	NOUN
iajs-3728	139	20	:	:	PUNCT
iajs-3728	139	21	a	a	X
iajs-3728	139	22	)	)	PUNCT
iajs-3728	139	23	dolphin	dolphin	NOUN
iajs-3728	139	24	network	network	NOUN
iajs-3728	139	25	,	,	PUNCT
iajs-3728	139	26	b	b	NOUN
iajs-3728	139	27	)	)	PUNCT
iajs-3728	139	28	karate	karate	NOUN
iajs-3728	139	29	c)afc	c)afc	PROPN
iajs-3728	139	30	d	d	PROPN
iajs-3728	139	31	)	)	PUNCT
iajs-3728	139	32	delaunay	delaunay	PROPN
iajs-3728	139	33	synthetic	synthetic	ADJ
iajs-3728	139	34	table	table	NOUN
iajs-3728	139	35	2	2	NUM
iajs-3728	139	36	.	.	PUNCT
iajs-3728	139	37	model	model	NOUN
iajs-3728	139	38	selection	selection	NOUN
iajs-3728	139	39	for	for	ADP
iajs-3728	139	40	various	various	ADJ
iajs-3728	139	41	networks	network	NOUN
iajs-3728	139	42	with	with	ADP
iajs-3728	139	43	theirs	theirs	PROPN
iajs-3728	139	44	minimum	minimum	PROPN
iajs-3728	139	45	bic	bic	PROPN
iajs-3728	139	46	and	and	CCONJ
iajs-3728	139	47	associated	associate	VERB
iajs-3728	139	48	log	log	NOUN
iajs-3728	139	49	-	-	PUNCT
iajs-3728	139	50	likelihood	likelihood	NOUN
iajs-3728	139	51	value	value	NOUN
iajs-3728	139	52	using	use	VERB
iajs-3728	139	53	various	various	ADJ
iajs-3728	139	54	community	community	NOUN
iajs-3728	139	55	detection	detection	NOUN
iajs-3728	139	56	algorithms	algorithm	NOUN
iajs-3728	139	57	.	.	PUNCT
iajs-3728	140	1	network	network	NOUN
iajs-3728	140	2	rcmm	rcmm	VERB
iajs-3728	140	3	danon	danon	PROPN
iajs-3728	140	4	method	method	NOUN
iajs-3728	140	5	k	k	PROPN
iajs-3728	140	6	bic	bic	PROPN
iajs-3728	140	7	log	log	NOUN
iajs-3728	140	8	-	-	PUNCT
iajs-3728	140	9	likelihood	likelihood	NOUN
iajs-3728	140	10	k	k	PROPN
iajs-3728	140	11	bic	bic	PROPN
iajs-3728	140	12	log	log	NOUN
iajs-3728	140	13	-	-	PUNCT
iajs-3728	140	14	likelihood	likelihood	NOUN
iajs-3728	140	15	dolphin	dolphin	NOUN
iajs-3728	140	16	4	4	NUM
iajs-3728	140	17	3.3555×10	3.3555×10	NUM
iajs-3728	140	18	3	3	NUM
iajs-3728	140	19	-1.6117×10	-1.6117×10	PROPN
iajs-3728	140	20	3	3	NUM
iajs-3728	140	21	4	4	NUM
iajs-3728	140	22	3.3757×10	3.3757×10	NUM
iajs-3728	140	23	3	3	NUM
iajs-3728	140	24	-1.6218×10	-1.6218×10	SYM
iajs-3728	140	25	3	3	NUM
iajs-3728	140	26	karate	karate	NOUN
iajs-3728	140	27	2	2	NUM
iajs-3728	140	28	1.5177×10	1.5177×10	NUM
iajs-3728	140	29	3	3	NUM
iajs-3728	140	30	-7.3943×10	-7.3943×10	SYM
iajs-3728	140	31	2	2	NUM
iajs-3728	140	32	4	4	NUM
iajs-3728	140	33	1.5464×10	1.5464×10	NUM
iajs-3728	140	34	3	3	NUM
iajs-3728	140	35	-7.1679×10	-7.1679×10	SYM
iajs-3728	140	36	2	2	NUM
iajs-3728	140	37	american	american	PROPN
iajs-3728	140	38	football	football	NOUN
iajs-3728	140	39	college	college	PROPN
iajs-3728	140	40	(	(	PUNCT
iajs-3728	140	41	afc	afc	PROPN
iajs-3728	140	42	)	)	PUNCT
iajs-3728	140	43	10	10	NUM
iajs-3728	140	44	1.5450×10	1.5450×10	NUM
iajs-3728	140	45	4	4	NUM
iajs-3728	140	46	-7.3286e×10	-7.3286e×10	SYM
iajs-3728	140	47	3	3	NUM
iajs-3728	140	48	7	7	NUM
iajs-3728	140	49	1.5662×10	1.5662×10	NUM
iajs-3728	140	50	4	4	NUM
iajs-3728	140	51	-7.6268×10	-7.6268×10	PROPN
iajs-3728	140	52	3	3	NUM
iajs-3728	140	53	delaunay	delaunay	PROPN
iajs-3728	140	54	synthetic	synthetic	NOUN
iajs-3728	140	55	19	19	NUM
iajs-3728	140	56	8.1922×10	8.1922×10	NUM
iajs-3728	140	57	4	4	NUM
iajs-3728	140	58	-3.8976×10	-3.8976×10	SYM
iajs-3728	140	59	4	4	NUM
iajs-3728	140	60	9	9	NUM
iajs-3728	140	61	8.6799×10	8.6799×10	NUM
iajs-3728	140	62	4	4	NUM
iajs-3728	140	63	-4.2925×10	-4.2925×10	NUM
iajs-3728	140	64	4	4	NUM
iajs-3728	140	65	chesapeake	chesapeake	NOUN
iajs-3728	140	66	synthetic	synthetic	NOUN
iajs-3728	140	67	2	2	NUM
iajs-3728	141	1	3.9148×10	3.9148×10	NUM
iajs-3728	141	2	3	3	NUM
iajs-3728	141	3	-1.9373×10	-1.9373×10	SYM
iajs-3728	141	4	3	3	NUM
iajs-3728	141	5	4	4	NUM
iajs-3728	141	6	4.0696×10	4.0696×10	NUM
iajs-3728	141	7	3	3	NUM
iajs-3728	141	8	-1.9762×10	-1.9762×10	PROPN
iajs-3728	141	9	3	3	NUM
iajs-3728	141	10	network	network	PROPN
iajs-3728	141	11	cmm	cmm	PROPN
iajs-3728	141	12	newman	newman	PROPN
iajs-3728	141	13	modularity	modularity	PROPN
iajs-3728	141	14	maximization	maximization	PROPN
iajs-3728	141	15	k	k	PROPN
iajs-3728	141	16	bic	bic	PROPN
iajs-3728	141	17	log	log	NOUN
iajs-3728	141	18	-	-	PUNCT
iajs-3728	141	19	likelihood	likelihood	NOUN
iajs-3728	141	20	k	k	PROPN
iajs-3728	141	21	bic	bic	PROPN
iajs-3728	141	22	log	log	NOUN
iajs-3728	141	23	-	-	PUNCT
iajs-3728	141	24	likelihood	likelihood	NOUN
iajs-3728	141	25	dolphin	dolphin	NOUN
iajs-3728	141	26	4	4	NUM
iajs-3728	141	27	3.3557×10	3.3557×10	NUM
iajs-3728	141	28	3	3	NUM
iajs-3728	141	29	-1.6118×10	-1.6118×10	PROPN
iajs-3728	141	30	3	3	NUM
iajs-3728	141	31	4	4	NUM
iajs-3728	141	32	3.4093×10	3.4093×10	NUM
iajs-3728	141	33	3	3	NUM
iajs-3728	141	34	-1.6386×10	-1.6386×10	NUM
iajs-3728	141	35	3	3	NUM
iajs-3728	141	36	karate	karate	NOUN
iajs-3728	141	37	2	2	NUM
iajs-3728	141	38	1.5177×10	1.5177×10	NUM
iajs-3728	141	39	3	3	NUM
iajs-3728	141	40	-7.3943×10	-7.3943×10	SYM
iajs-3728	141	41	2	2	NUM
iajs-3728	141	42	4	4	NUM
iajs-3728	141	43	1.5735×10	1.5735×10	NUM
iajs-3728	141	44	3	3	NUM
iajs-3728	141	45	-7.30324×10	-7.30324×10	SYM
iajs-3728	141	46	2	2	NUM
iajs-3728	141	47	american	american	PROPN
iajs-3728	141	48	football	football	PROPN
iajs-3728	141	49	college	college	PROPN
iajs-3728	141	50	(	(	PUNCT
iajs-3728	141	51	afc	afc	PROPN
iajs-3728	141	52	)	)	PUNCT
iajs-3728	141	53	10	10	NUM
iajs-3728	141	54	1.5450×10	1.5450×10	NUM
iajs-3728	141	55	4	4	NUM
iajs-3728	141	56	-7.3286×10	-7.3286×10	NUM
iajs-3728	141	57	3	3	NUM
iajs-3728	141	58	7	7	NUM
iajs-3728	141	59	1.5662×10	1.5662×10	NUM
iajs-3728	141	60	4	4	NUM
iajs-3728	141	61	-7.6268×10	-7.6268×10	PROPN
iajs-3728	141	62	3	3	NUM
iajs-3728	141	63	delaunay	delaunay	PROPN
iajs-3728	141	64	synthetic	synthetic	NOUN
iajs-3728	141	65	19	19	NUM
iajs-3728	141	66	8.1885×10	8.1885×10	NUM
iajs-3728	141	67	4	4	NUM
iajs-3728	141	68	-3.8942×10	-3.8942×10	NUM
iajs-3728	141	69	4	4	NUM
iajs-3728	141	70	7	7	NUM
iajs-3728	141	71	8.9932×10	8.9932×10	NUM
iajs-3728	141	72	4	4	NUM
iajs-3728	141	73	-4.4668×10	-4.4668×10	SYM
iajs-3728	141	74	4	4	NUM
iajs-3728	141	75	chesapeake	chesapeake	NOUN
iajs-3728	141	76	synthetic	synthetic	NOUN
iajs-3728	141	77	3	3	NUM
iajs-3728	141	78	3.9262×10	3.9262×10	NUM
iajs-3728	141	79	3	3	NUM
iajs-3728	141	80	-1.9265×10	-1.9265×10	SYM
iajs-3728	141	81	3	3	NUM
iajs-3728	141	82	3	3	NUM
iajs-3728	141	83	3.9294×10	3.9294×10	NUM
iajs-3728	141	84	3	3	NUM
iajs-3728	141	85	-1.9281×10	-1.9281×10	SYM
iajs-3728	141	86	3	3	NUM
iajs-3728	141	87	network	network	NOUN
iajs-3728	141	88	spectral	spectral	ADJ
iajs-3728	141	89	clustering	cluster	VERB
iajs-3728	141	90	k	k	PROPN
iajs-3728	141	91	bic	bic	PROPN
iajs-3728	141	92	log	log	NOUN
iajs-3728	141	93	-	-	PUNCT
iajs-3728	141	94	likelihood	likelihood	NOUN
iajs-3728	141	95	dolphin	dolphin	NOUN
iajs-3728	141	96	4	4	NUM
iajs-3728	141	97	3.3940×10	3.3940×10	NUM
iajs-3728	141	98	3	3	NUM
iajs-3728	141	99	-1.6310	-1.6310	NOUN
iajs-3728	141	100	×10	×10	PROPN
iajs-3728	141	101	3	3	NUM
iajs-3728	141	102	karate	karate	NOUN
iajs-3728	141	103	3	3	NUM
iajs-3728	141	104	1.5119×10	1.5119×10	NUM
iajs-3728	141	105	3	3	NUM
iajs-3728	141	106	-7.2069×10	-7.2069×10	SYM
iajs-3728	141	107	2	2	NUM
iajs-3728	141	108	american	american	PROPN
iajs-3728	141	109	football	football	PROPN
iajs-3728	141	110	college	college	PROPN
iajs-3728	141	111	(	(	PUNCT
iajs-3728	141	112	afc	afc	PROPN
iajs-3728	141	113	)	)	PUNCT
iajs-3728	141	114	8	8	NUM
iajs-3728	141	115	1.5686×10	1.5686×10	NUM
iajs-3728	141	116	4	4	NUM
iajs-3728	141	117	-7.5821×10	-7.5821×10	PROPN
iajs-3728	141	118	3	3	NUM
iajs-3728	141	119	delaunay	delaunay	PROPN
iajs-3728	141	120	synthetic	synthetic	NOUN
iajs-3728	141	121	19	19	NUM
iajs-3728	141	122	8.268×10	8.268×10	NUM
iajs-3728	141	123	4	4	NUM
iajs-3728	141	124	-3.9358×10	-3.9358×10	NUM
iajs-3728	141	125	4	4	NUM
iajs-3728	141	126	chesapeake	chesapeake	NOUN
iajs-3728	141	127	synthetic	synthetic	NOUN
iajs-3728	141	128	4	4	NUM
iajs-3728	141	129	3.9553×10	3.9553×10	NUM
iajs-3728	141	130	3	3	NUM
iajs-3728	141	131	-1.9190×10	-1.9190×10	SYM
iajs-3728	141	132	3	3	NUM
iajs-3728	141	133	ihjpas	ihjpas	PROPN
iajs-3728	141	134	.	.	PUNCT
iajs-3728	142	1	2025	2025	NUM
iajs-3728	142	2	,	,	PUNCT
iajs-3728	142	3	38(3	38(3	NUM
iajs-3728	142	4	)	)	PUNCT
iajs-3728	142	5	334	334	NUM
iajs-3728	142	6	6	6	NUM
iajs-3728	142	7	.	.	PUNCT
iajs-3728	142	8	conclusion	conclusion	NOUN
iajs-3728	142	9	a	a	DET
iajs-3728	142	10	model	model	NOUN
iajs-3728	142	11	selection	selection	NOUN
iajs-3728	142	12	for	for	ADP
iajs-3728	142	13	stochastic	stochastic	ADJ
iajs-3728	142	14	block	block	NOUN
iajs-3728	142	15	models	model	NOUN
iajs-3728	142	16	based	base	VERB
iajs-3728	142	17	on	on	ADP
iajs-3728	142	18	the	the	DET
iajs-3728	142	19	optimization	optimization	NOUN
iajs-3728	142	20	of	of	ADP
iajs-3728	142	21	the	the	DET
iajs-3728	142	22	log_likelihood	log_likelihood	ADJ
iajs-3728	142	23	function	function	NOUN
iajs-3728	142	24	is	be	AUX
iajs-3728	142	25	given	give	VERB
iajs-3728	142	26	for	for	ADP
iajs-3728	142	27	the	the	DET
iajs-3728	142	28	communities	community	NOUN
iajs-3728	142	29	detected	detect	VERB
iajs-3728	142	30	by	by	ADP
iajs-3728	142	31	the	the	DET
iajs-3728	142	32	rcmm	rcmm	PROPN
iajs-3728	142	33	method	method	NOUN
iajs-3728	142	34	under	under	ADP
iajs-3728	142	35	degree	degree	NOUN
iajs-3728	142	36	-	-	PUNCT
iajs-3728	142	37	corrected	correct	VERB
iajs-3728	142	38	sbm	sbm	NOUN
iajs-3728	142	39	to	to	PART
iajs-3728	142	40	find	find	VERB
iajs-3728	142	41	the	the	DET
iajs-3728	142	42	best	good	ADJ
iajs-3728	142	43	number	number	NOUN
iajs-3728	142	44	k	k	PROPN
iajs-3728	142	45	of	of	ADP
iajs-3728	142	46	communities	community	NOUN
iajs-3728	142	47	that	that	PRON
iajs-3728	142	48	have	have	VERB
iajs-3728	142	49	the	the	DET
iajs-3728	142	50	minimum	minimum	ADJ
iajs-3728	142	51	bic	bic	NOUN
iajs-3728	142	52	.	.	PUNCT
iajs-3728	143	1	the	the	DET
iajs-3728	143	2	results	result	NOUN
iajs-3728	143	3	are	be	AUX
iajs-3728	143	4	compared	compare	VERB
iajs-3728	143	5	with	with	ADP
iajs-3728	143	6	the	the	DET
iajs-3728	143	7	results	result	NOUN
iajs-3728	143	8	of	of	ADP
iajs-3728	143	9	other	other	ADJ
iajs-3728	143	10	different	different	ADJ
iajs-3728	143	11	methods	method	NOUN
iajs-3728	143	12	,	,	PUNCT
iajs-3728	143	13	including	include	VERB
iajs-3728	143	14	cmm	cmm	NOUN
iajs-3728	143	15	under	under	ADP
iajs-3728	143	16	degree	degree	NOUN
iajs-3728	143	17	-	-	PUNCT
iajs-3728	143	18	corrected	correct	VERB
iajs-3728	143	19	sbm	sbm	NOUN
iajs-3728	143	20	,	,	PUNCT
iajs-3728	143	21	danon	danon	ADJ
iajs-3728	143	22	method	method	NOUN
iajs-3728	143	23	,	,	PUNCT
iajs-3728	143	24	newman	newman	PROPN
iajs-3728	143	25	modularity	modularity	PROPN
iajs-3728	143	26	maximization	maximization	PROPN
iajs-3728	143	27	,	,	PUNCT
iajs-3728	143	28	and	and	CCONJ
iajs-3728	143	29	spectral	spectral	ADJ
iajs-3728	143	30	clustering	clustering	NOUN
iajs-3728	143	31	,	,	PUNCT
iajs-3728	143	32	and	and	CCONJ
iajs-3728	143	33	applied	apply	VERB
iajs-3728	143	34	to	to	ADP
iajs-3728	143	35	both	both	CCONJ
iajs-3728	143	36	real	real	ADJ
iajs-3728	143	37	and	and	CCONJ
iajs-3728	143	38	synthetic	synthetic	ADJ
iajs-3728	143	39	networks	network	NOUN
iajs-3728	143	40	.	.	PUNCT
iajs-3728	144	1	the	the	DET
iajs-3728	144	2	results	result	NOUN
iajs-3728	144	3	show	show	VERB
iajs-3728	144	4	that	that	SCONJ
iajs-3728	144	5	model	model	NOUN
iajs-3728	144	6	selection	selection	NOUN
iajs-3728	144	7	for	for	ADP
iajs-3728	144	8	many	many	ADJ
iajs-3728	144	9	networks	network	NOUN
iajs-3728	144	10	partitioned	partition	VERB
iajs-3728	144	11	with	with	ADP
iajs-3728	144	12	rcmm	rcmm	NOUN
iajs-3728	144	13	using	use	VERB
iajs-3728	144	14	degreecorrected	degreecorrecte	VERB
iajs-3728	144	15	sbm	sbm	NOUN
iajs-3728	144	16	is	be	AUX
iajs-3728	144	17	identical	identical	ADJ
iajs-3728	144	18	to	to	ADP
iajs-3728	144	19	many	many	ADJ
iajs-3728	144	20	results	result	NOUN
iajs-3728	144	21	obtained	obtain	VERB
iajs-3728	144	22	with	with	ADP
iajs-3728	144	23	other	other	ADJ
iajs-3728	144	24	community	community	NOUN
iajs-3728	144	25	detection	detection	NOUN
iajs-3728	144	26	methods	method	NOUN
iajs-3728	144	27	,	,	PUNCT
iajs-3728	144	28	and	and	CCONJ
iajs-3728	144	29	that	that	SCONJ
iajs-3728	144	30	in	in	ADP
iajs-3728	144	31	most	most	ADJ
iajs-3728	144	32	cases	case	NOUN
iajs-3728	144	33	it	it	PRON
iajs-3728	144	34	is	be	AUX
iajs-3728	144	35	better	well	ADJ
iajs-3728	144	36	because	because	SCONJ
iajs-3728	144	37	it	it	PRON
iajs-3728	144	38	has	have	VERB
iajs-3728	144	39	a	a	DET
iajs-3728	144	40	lower	low	ADJ
iajs-3728	144	41	bic	bic	NOUN
iajs-3728	144	42	value	value	NOUN
iajs-3728	144	43	,	,	PUNCT
iajs-3728	144	44	so	so	SCONJ
iajs-3728	144	45	it	it	PRON
iajs-3728	144	46	fits	fit	VERB
iajs-3728	144	47	the	the	DET
iajs-3728	144	48	observed	observe	VERB
iajs-3728	144	49	data	datum	NOUN
iajs-3728	144	50	better	well	ADV
iajs-3728	144	51	.	.	PUNCT
iajs-3728	145	1	moreover	moreover	ADV
iajs-3728	145	2	,	,	PUNCT
iajs-3728	145	3	rcmm	rcmm	PROPN
iajs-3728	145	4	is	be	AUX
iajs-3728	145	5	able	able	ADJ
iajs-3728	145	6	to	to	PART
iajs-3728	145	7	detect	detect	VERB
iajs-3728	145	8	small	small	ADJ
iajs-3728	145	9	communities	community	NOUN
iajs-3728	145	10	as	as	SCONJ
iajs-3728	145	11	it	it	PRON
iajs-3728	145	12	analyzes	analyze	VERB
iajs-3728	145	13	the	the	DET
iajs-3728	145	14	networks	network	NOUN
iajs-3728	145	15	with	with	ADP
iajs-3728	145	16	a	a	DET
iajs-3728	145	17	larger	large	ADJ
iajs-3728	145	18	value	value	NOUN
iajs-3728	145	19	of	of	ADP
iajs-3728	145	20	k.	k.	PROPN
iajs-3728	145	21	however	however	ADV
iajs-3728	145	22	,	,	PUNCT
iajs-3728	145	23	the	the	DET
iajs-3728	145	24	goal	goal	NOUN
iajs-3728	145	25	of	of	ADP
iajs-3728	145	26	model	model	NOUN
iajs-3728	145	27	selection	selection	NOUN
iajs-3728	145	28	is	be	AUX
iajs-3728	145	29	to	to	PART
iajs-3728	145	30	find	find	VERB
iajs-3728	145	31	a	a	DET
iajs-3728	145	32	model	model	NOUN
iajs-3728	145	33	that	that	PRON
iajs-3728	145	34	provides	provide	VERB
iajs-3728	145	35	both	both	DET
iajs-3728	145	36	a	a	DET
iajs-3728	145	37	good	good	ADJ
iajs-3728	145	38	fit	fit	NOUN
iajs-3728	145	39	to	to	ADP
iajs-3728	145	40	the	the	DET
iajs-3728	145	41	existing	exist	VERB
iajs-3728	145	42	observations	observation	NOUN
iajs-3728	145	43	and	and	CCONJ
iajs-3728	145	44	reasonable	reasonable	ADJ
iajs-3728	145	45	predictions	prediction	NOUN
iajs-3728	145	46	for	for	ADP
iajs-3728	145	47	future	future	ADJ
iajs-3728	145	48	observations	observation	NOUN
iajs-3728	145	49	.	.	PUNCT
iajs-3728	146	1	acknowledgment	acknowledgment	NOUN
iajs-3728	146	2	our	our	PRON
iajs-3728	146	3	researcher	researcher	NOUN
iajs-3728	146	4	extends	extend	VERB
iajs-3728	146	5	his	his	PRON
iajs-3728	146	6	sincere	sincere	ADJ
iajs-3728	146	7	thanks	thank	NOUN
iajs-3728	146	8	to	to	ADP
iajs-3728	146	9	the	the	DET
iajs-3728	146	10	editor	editor	NOUN
iajs-3728	146	11	and	and	CCONJ
iajs-3728	146	12	members	member	NOUN
iajs-3728	146	13	of	of	ADP
iajs-3728	146	14	the	the	DET
iajs-3728	146	15	preparatory	preparatory	PROPN
iajs-3728	146	16	committee	committee	NOUN
iajs-3728	146	17	of	of	ADP
iajs-3728	146	18	the	the	DET
iajs-3728	146	19	ibn	ibn	PROPN
iajs-3728	146	20	al	al	PROPN
iajs-3728	146	21	-	-	PUNCT
iajs-3728	146	22	haitham	haitham	PROPN
iajs-3728	146	23	journal	journal	PROPN
iajs-3728	146	24	of	of	ADP
iajs-3728	146	25	pure	pure	ADJ
iajs-3728	146	26	and	and	CCONJ
iajs-3728	146	27	applied	applied	ADJ
iajs-3728	146	28	sciences	science	NOUN
iajs-3728	146	29	.	.	PUNCT
iajs-3728	147	1	conflict	conflict	NOUN
iajs-3728	147	2	of	of	ADP
iajs-3728	147	3	interest	interest	NOUN
iajs-3728	147	4	there	there	PRON
iajs-3728	147	5	are	be	VERB
iajs-3728	147	6	no	no	DET
iajs-3728	147	7	conflicts	conflict	NOUN
iajs-3728	147	8	of	of	ADP
iajs-3728	147	9	interest	interest	NOUN
iajs-3728	147	10	.	.	PUNCT
iajs-3728	148	1	funding	funding	NOUN
iajs-3728	148	2	there	there	PRON
iajs-3728	148	3	is	be	VERB
iajs-3728	148	4	no	no	DET
iajs-3728	148	5	funding	funding	NOUN
iajs-3728	148	6	for	for	ADP
iajs-3728	148	7	the	the	DET
iajs-3728	148	8	article	article	NOUN
iajs-3728	148	9	.	.	PUNCT
iajs-3728	149	1	references	reference	NOUN
iajs-3728	149	2	1	1	NUM
iajs-3728	149	3	.	.	PUNCT
iajs-3728	149	4	karrer	karrer	PROPN
iajs-3728	149	5	b.	b.	PROPN
iajs-3728	149	6	,	,	PUNCT
iajs-3728	149	7	newman	newman	PROPN
iajs-3728	149	8	m.e.j	m.e.j	PROPN
iajs-3728	149	9	.	.	PUNCT
iajs-3728	150	1	stochastic	stochastic	ADJ
iajs-3728	150	2	blockmodels	blockmodel	NOUN
iajs-3728	150	3	and	and	CCONJ
iajs-3728	150	4	community	community	NOUN
iajs-3728	150	5	structure	structure	NOUN
iajs-3728	150	6	in	in	ADP
iajs-3728	150	7	networks	network	NOUN
iajs-3728	150	8	.	.	PUNCT
iajs-3728	151	1	phys	phy	NOUN
iajs-3728	151	2	rev	rev	VERB
iajs-3728	151	3	e	e	PROPN
iajs-3728	151	4	stat	stat	PROPN
iajs-3728	151	5	nonlin	nonlin	PROPN
iajs-3728	151	6	soft	soft	ADJ
iajs-3728	151	7	matter	matter	NOUN
iajs-3728	151	8	phys	phy	NOUN
iajs-3728	151	9	2011;83(1):1–11.https://doi.org/10.1103	2011;83(1):1–11.https://doi.org/10.1103	NUM
iajs-3728	151	10	/	/	SYM
iajs-3728	151	11	physreve.83.016107	physreve.83.016107	NOUN
iajs-3728	151	12	2	2	NUM
iajs-3728	151	13	.	.	PUNCT
iajs-3728	151	14	abdul	abdul	PROPN
iajs-3728	151	15	-	-	PUNCT
iajs-3728	151	16	emeer	emeer	PROPN
iajs-3728	151	17	s.s	s.s	PROPN
iajs-3728	151	18	.	.	PUNCT
iajs-3728	152	1	an	an	DET
iajs-3728	152	2	evolutionary	evolutionary	ADJ
iajs-3728	152	3	bi	bi	ADJ
iajs-3728	152	4	-	-	ADJ
iajs-3728	152	5	clustering	clustering	ADJ
iajs-3728	152	6	algorithm	algorithm	NOUN
iajs-3728	152	7	for	for	ADP
iajs-3728	152	8	community	community	NOUN
iajs-3728	152	9	detection	detection	NOUN
iajs-3728	152	10	in	in	ADP
iajs-3728	152	11	social	social	ADJ
iajs-3728	152	12	networks	network	NOUN
iajs-3728	152	13	.	.	PUNCT
iajs-3728	153	1	iraqi	iraqi	ADJ
iajs-3728	153	2	j	j	PROPN
iajs-3728	153	3	sci	sci	PROPN
iajs-3728	153	4	2016;57(3):2111–2120	2016;57(3):2111–2120	PROPN
iajs-3728	153	5	.	.	PUNCT
iajs-3728	154	1	3	3	X
iajs-3728	154	2	.	.	PUNCT
iajs-3728	154	3	mohammed	mohammed	PROPN
iajs-3728	154	4	s.k	s.k	PROPN
iajs-3728	154	5	.	.	PROPN
iajs-3728	154	6	,	,	PUNCT
iajs-3728	154	7	taha	taha	PROPN
iajs-3728	154	8	m.m	m.m	PROPN
iajs-3728	154	9	.	.	PROPN
iajs-3728	154	10	,	,	PUNCT
iajs-3728	154	11	taha	taha	PROPN
iajs-3728	154	12	e.m	e.m	PROPN
iajs-3728	154	13	.	.	PROPN
iajs-3728	154	14	,	,	PUNCT
iajs-3728	154	15	mohammad	mohammad	PROPN
iajs-3728	154	16	m.n.a	m.n.a	PROPN
iajs-3728	154	17	.	.	PUNCT
iajs-3728	155	1	cluster	cluster	NOUN
iajs-3728	155	2	analysis	analysis	NOUN
iajs-3728	155	3	of	of	ADP
iajs-3728	155	4	biochemical	biochemical	ADJ
iajs-3728	155	5	markers	marker	NOUN
iajs-3728	155	6	as	as	ADP
iajs-3728	155	7	predictor	predictor	NOUN
iajs-3728	155	8	of	of	ADP
iajs-3728	155	9	covid-19	covid-19	PROPN
iajs-3728	155	10	severity	severity	NOUN
iajs-3728	155	11	.	.	PUNCT
iajs-3728	156	1	baghdad	baghdad	PROPN
iajs-3728	156	2	sci	sci	PROPN
iajs-3728	157	1	j	j	PROPN
iajs-3728	157	2	2022;19(6):1423–1429	2022;19(6):1423–1429	PROPN
iajs-3728	157	3	.	.	PUNCT
iajs-3728	158	1	https://doi.org/10.21123/bsj.2022.7454	https://doi.org/10.21123/bsj.2022.7454	NOUN
iajs-3728	158	2	4	4	NUM
iajs-3728	158	3	.	.	PUNCT
iajs-3728	158	4	li	li	PROPN
iajs-3728	158	5	x.	x.	PROPN
iajs-3728	158	6	,	,	PUNCT
iajs-3728	158	7	chen	chen	PROPN
iajs-3728	158	8	y.	y.	PROPN
iajs-3728	158	9	,	,	PUNCT
iajs-3728	158	10	xu	xu	PROPN
iajs-3728	158	11	j.	j.	PROPN
iajs-3728	158	12	convex	convex	PROPN
iajs-3728	158	13	relaxation	relaxation	NOUN
iajs-3728	158	14	methods	method	NOUN
iajs-3728	158	15	for	for	ADP
iajs-3728	158	16	community	community	NOUN
iajs-3728	158	17	detection	detection	NOUN
iajs-3728	158	18	.	.	PUNCT
iajs-3728	159	1	stat	stat	PROPN
iajs-3728	159	2	sci	sci	PROPN
iajs-3728	159	3	2021;36(1):2	2021;36(1):2	NUM
iajs-3728	159	4	–	–	PUNCT
iajs-3728	159	5	15	15	NUM
iajs-3728	159	6	.	.	PUNCT
iajs-3728	159	7	https://doi.org/10.1214/19-sts715	https://doi.org/10.1214/19-sts715	NOUN
iajs-3728	159	8	5	5	NUM
iajs-3728	159	9	.	.	PUNCT
iajs-3728	160	1	holland	holland	PROPN
iajs-3728	160	2	p.w	p.w	PROPN
iajs-3728	160	3	.	.	PROPN
iajs-3728	160	4	,	,	PUNCT
iajs-3728	160	5	laskey	laskey	PROPN
iajs-3728	160	6	k.b	k.b	PROPN
iajs-3728	160	7	.	.	PROPN
iajs-3728	160	8	,	,	PUNCT
iajs-3728	160	9	leinhardt	leinhardt	PROPN
iajs-3728	160	10	s.	s.	PROPN
iajs-3728	160	11	stochastic	stochastic	PROPN
iajs-3728	160	12	blockmodels	blockmodel	NOUN
iajs-3728	160	13	:	:	PUNCT
iajs-3728	160	14	first	first	ADJ
iajs-3728	160	15	steps	step	NOUN
iajs-3728	160	16	.	.	PUNCT
iajs-3728	161	1	soc	soc	NOUN
iajs-3728	161	2	networks	network	VERB
iajs-3728	161	3	1983;5(2):109–137	1983;5(2):109–137	NUM
iajs-3728	161	4	.	.	PUNCT
iajs-3728	162	1	https://doi.org/10.1016/0378-8733(83)90021-7	https://doi.org/10.1016/0378-8733(83)90021-7	PROPN
iajs-3728	162	2	.	.	PUNCT
iajs-3728	163	1	6	6	X
iajs-3728	163	2	.	.	X
iajs-3728	163	3	alsaleem	alsaleem	PROPN
iajs-3728	163	4	n.y.a	n.y.a	PROPN
iajs-3728	163	5	.	.	PROPN
iajs-3728	163	6	,	,	PUNCT
iajs-3728	163	7	alsaleem	alsaleem	VERB
iajs-3728	163	8	m.y.a	m.y.a	PROPN
iajs-3728	163	9	.	.	PROPN
iajs-3728	163	10	,	,	PUNCT
iajs-3728	163	11	zakar	zakar	PROPN
iajs-3728	163	12	n.a	n.a	PROPN
iajs-3728	163	13	.	.	PROPN
iajs-3728	163	14	network	network	NOUN
iajs-3728	163	15	performance	performance	NOUN
iajs-3728	163	16	analysis	analysis	NOUN
iajs-3728	163	17	based	base	VERB
iajs-3728	163	18	on	on	ADP
iajs-3728	163	19	network	network	NOUN
iajs-3728	163	20	simulator	simulator	PROPN
iajs-3728	163	21	ns-2	ns-2	PROPN
iajs-3728	163	22	.	.	PUNCT
iajs-3728	164	1	ibn	ibn	PROPN
iajs-3728	164	2	al	al	PROPN
iajs-3728	164	3	-	-	PUNCT
iajs-3728	164	4	haitham	haitham	PROPN
iajs-3728	164	5	j	j	PROPN
iajs-3728	164	6	pure	pure	PROPN
iajs-3728	164	7	appl	appl	PROPN
iajs-3728	164	8	sci	sci	PROPN
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iajs-3728	164	10	.	.	PUNCT
iajs-3728	165	1	7	7	X
iajs-3728	165	2	.	.	X
iajs-3728	165	3	hassan	hassan	PROPN
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iajs-3728	165	8	.	.	PROPN
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iajs-3728	165	11	networks	network	NOUN
iajs-3728	165	12	using	use	VERB
iajs-3728	165	13	data	datum	NOUN
iajs-3728	165	14	mining	mining	NOUN
iajs-3728	165	15	approaches	approach	NOUN
iajs-3728	165	16	—	—	PUNCT
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iajs-3728	165	18	.	.	PUNCT
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iajs-3728	166	2	j	j	PROPN
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iajs-3728	166	5	.	.	PUNCT
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iajs-3728	167	3	.	.	PUNCT
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iajs-3728	168	7	.	.	PUNCT
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iajs-3728	168	13	for	for	ADP
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iajs-3728	168	17	.	.	PUNCT
iajs-3728	169	1	ann	ann	PROPN
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iajs-3728	169	4	.	.	PUNCT
iajs-3728	170	1	https://doi.org/10.1214/16-aos1457	https://doi.org/10.1214/16-aos1457	PROPN
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iajs-3728	170	3	.	.	PUNCT
iajs-3728	171	1	al	al	ADJ
iajs-3728	171	2	-	-	PUNCT
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iajs-3728	171	5	.	.	PUNCT
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iajs-3728	172	10	by	by	ADP
iajs-3728	172	11	using	use	VERB
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iajs-3728	172	14	loss	loss	NOUN
iajs-3728	172	15	functions	function	NOUN
iajs-3728	172	16	.	.	PUNCT
iajs-3728	173	1	ibn	ibn	PROPN
iajs-3728	173	2	al	al	PROPN
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iajs-3728	173	4	haitham	haitham	PROPN
iajs-3728	173	5	j	j	PROPN
iajs-3728	173	6	pure	pure	PROPN
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iajs-3728	173	8	sci	sci	PROPN
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iajs-3728	173	10	.	.	PUNCT
iajs-3728	174	1	https://doi.org/10.30526/35.1.2800	https://doi.org/10.30526/35.1.2800	PROPN
iajs-3728	174	2	10	10	NUM
iajs-3728	174	3	.	.	PUNCT
iajs-3728	175	1	abbe	abbe	PROPN
iajs-3728	175	2	e.	e.	PROPN
iajs-3728	175	3	community	community	PROPN
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iajs-3728	175	5	and	and	CCONJ
iajs-3728	175	6	stochastic	stochastic	ADJ
iajs-3728	175	7	block	block	NOUN
iajs-3728	175	8	models	model	NOUN
iajs-3728	175	9	:	:	PUNCT
iajs-3728	175	10	recent	recent	ADJ
iajs-3728	175	11	developments	development	NOUN
iajs-3728	175	12	.	.	PUNCT
iajs-3728	176	1	j	j	PROPN
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iajs-3728	176	6	.	.	PROPN
iajs-3728	177	1	11	11	NUM
iajs-3728	177	2	.	.	PUNCT
iajs-3728	177	3	von	von	PROPN
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iajs-3728	177	5	u.	u.	PROPN
iajs-3728	177	6	a	a	DET
iajs-3728	177	7	tutorial	tutorial	NOUN
iajs-3728	177	8	on	on	ADP
iajs-3728	177	9	spectral	spectral	ADJ
iajs-3728	177	10	clustering	clustering	NOUN
iajs-3728	177	11	.	.	PUNCT
iajs-3728	178	1	stat	stat	PROPN
iajs-3728	178	2	comput	comput	PROPN
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iajs-3728	178	4	.	.	PUNCT
iajs-3728	179	1	https://doi.org/10.1007/s11222-007-9033-z	https://doi.org/10.1007/s11222-007-9033-z	PROPN
iajs-3728	179	2	https://doi.org/10.1103/physreve.83.016107	https://doi.org/10.1103/physreve.83.016107	ADJ
iajs-3728	179	3	https://doi.org/10.21123/bsj.2022.7454	https://doi.org/10.21123/bsj.2022.7454	NOUN
iajs-3728	179	4	https://doi.org/10.1214/19-sts715	https://doi.org/10.1214/19-sts715	NOUN
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iajs-3728	179	9	https://doi.org/10.1007/s11222-007-9033-z	https://doi.org/10.1007/s11222-007-9033-z	PROPN
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iajs-3728	179	11	.	.	PUNCT
iajs-3728	180	1	2025	2025	NUM
iajs-3728	180	2	,	,	PUNCT
iajs-3728	180	3	38(3	38(3	NUM
iajs-3728	180	4	)	)	PUNCT
iajs-3728	180	5	335	335	NUM
iajs-3728	180	6	12	12	NUM
iajs-3728	180	7	.	.	PUNCT
iajs-3728	181	1	dabbs	dabbs	PROPN
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iajs-3728	181	9	-	-	ADJ
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iajs-3728	181	12	for	for	ADP
iajs-3728	181	13	stochastic	stochastic	ADJ
iajs-3728	181	14	block	block	NOUN
iajs-3728	181	15	models	model	NOUN
iajs-3728	181	16	.	.	PUNCT
iajs-3728	182	1	arxiv	arxiv	PROPN
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iajs-3728	182	4	.	.	PUNCT
iajs-3728	183	1	http://arxiv.org/abs/1605.03000	http://arxiv.org/abs/1605.03000	PROPN
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iajs-3728	183	3	.	.	PUNCT
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iajs-3728	184	3	.	.	PUNCT
iajs-3728	185	1	networks	network	NOUN
iajs-3728	185	2	:	:	PUNCT
iajs-3728	185	3	an	an	DET
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iajs-3728	185	5	.	.	PUNCT
iajs-3728	186	1	oxford	oxford	PROPN
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iajs-3728	186	5	.	.	PUNCT
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iajs-3728	187	4	.	.	PUNCT
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iajs-3728	188	2	.	.	PUNCT
iajs-3728	189	1	nishii	nishii	PROPN
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iajs-3728	189	12	model	model	NOUN
iajs-3728	189	13	is	be	AUX
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iajs-3728	189	15	.	.	PUNCT
iajs-3728	190	1	j	j	PROPN
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iajs-3728	190	5	.	.	PUNCT
iajs-3728	191	1	https://doi.org/10.1016/0047-259x(88)90137-6	https://doi.org/10.1016/0047-259x(88)90137-6	PROPN
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iajs-3728	191	3	.	.	PUNCT
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iajs-3728	192	15	in	in	ADP
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iajs-3728	192	18	.	.	PUNCT
iajs-3728	193	1	2021	2021	NUM
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iajs-3728	193	10	.	.	PUNCT
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iajs-3728	193	13	.	.	PUNCT
iajs-3728	194	1	yaqoob	yaqoob	VERB
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iajs-3728	194	7	sarray	sarray	PROPN
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iajs-3728	194	14	model	model	NOUN
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iajs-3728	194	23	-	-	PUNCT
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iajs-3728	194	30	.	.	PUNCT
iajs-3728	195	1	iraqi	iraqi	PROPN
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iajs-3728	195	5	.	.	PUNCT
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iajs-3728	195	8	.	.	PUNCT
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iajs-3728	197	7	.	.	PUNCT
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iajs-3728	197	10	,	,	PUNCT
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iajs-3728	197	17	stochastic	stochastic	ADJ
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iajs-3728	197	19	models	model	NOUN
iajs-3728	197	20	:	:	PUNCT
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iajs-3728	198	9	–	–	PUNCT
iajs-3728	198	10	688	688	NUM
iajs-3728	198	11	.	.	PUNCT
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iajs-3728	199	3	.	.	PUNCT
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iajs-3728	200	9	a	a	DET
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iajs-3728	201	1	stat	stat	PROPN
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iajs-3728	201	4	–	–	PUNCT
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iajs-3728	201	8	.	.	PUNCT
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iajs-3728	202	7	sarray	sarray	PROPN
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iajs-3728	204	5	.	.	PUNCT
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iajs-3728	205	3	.	.	PUNCT
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iajs-3728	206	6	,	,	PUNCT
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iajs-3728	206	8	j.	j.	PROPN
iajs-3728	206	9	convexified	convexifie	VERB
iajs-3728	206	10	modularity	modularity	NOUN
iajs-3728	206	11	maximization	maximization	NOUN
iajs-3728	206	12	for	for	ADP
iajs-3728	206	13	degree	degree	NOUN
iajs-3728	206	14	-	-	PUNCT
iajs-3728	206	15	corrected	correct	VERB
iajs-3728	206	16	stochastic	stochastic	ADJ
iajs-3728	206	17	block	block	NOUN
iajs-3728	206	18	models	model	NOUN
iajs-3728	206	19	.	.	PUNCT
iajs-3728	207	1	ann	ann	PROPN
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iajs-3728	207	4	.	.	PUNCT
iajs-3728	208	1	https://doi.org/10.1214/17-aos1595	https://doi.org/10.1214/17-aos1595	NOUN
iajs-3728	208	2	22	22	NUM
iajs-3728	208	3	.	.	PUNCT
iajs-3728	209	1	newman	newman	PROPN
iajs-3728	209	2	m.e.j	m.e.j	PROPN
iajs-3728	209	3	.	.	PUNCT
iajs-3728	210	1	fast	fast	ADJ
iajs-3728	210	2	algorithm	algorithm	NOUN
iajs-3728	210	3	for	for	ADP
iajs-3728	210	4	detecting	detect	VERB
iajs-3728	210	5	community	community	NOUN
iajs-3728	210	6	structure	structure	NOUN
iajs-3728	210	7	in	in	ADP
iajs-3728	210	8	networks	network	NOUN
iajs-3728	210	9	.	.	PUNCT
iajs-3728	211	1	phys	phy	NOUN
iajs-3728	211	2	rev	rev	VERB
iajs-3728	211	3	e	e	PROPN
iajs-3728	211	4	stat	stat	PROPN
iajs-3728	211	5	phys	phy	NOUN
iajs-3728	211	6	plasmas	plasma	NOUN
iajs-3728	211	7	fluids	fluid	NOUN
iajs-3728	211	8	relat	relat	PROPN
iajs-3728	211	9	interdiscip	interdiscip	VERB
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iajs-3728	211	12	.	.	PUNCT
iajs-3728	212	1	https://doi.org/10.1103/physreve.69.066133	https://doi.org/10.1103/physreve.69.066133	PROPN
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iajs-3728	212	3	.	.	PUNCT
iajs-3728	213	1	danon	danon	PROPN
iajs-3728	213	2	l.	l.	PROPN
iajs-3728	213	3	,	,	PUNCT
iajs-3728	213	4	díaz	díaz	NOUN
iajs-3728	213	5	-	-	PUNCT
iajs-3728	213	6	guilera	guilera	NOUN
iajs-3728	213	7	a.	a.	NOUN
iajs-3728	213	8	,	,	PUNCT
iajs-3728	213	9	arenas	arenas	PROPN
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iajs-3728	213	12	effect	effect	NOUN
iajs-3728	213	13	of	of	ADP
iajs-3728	213	14	size	size	NOUN
iajs-3728	213	15	heterogeneity	heterogeneity	NOUN
iajs-3728	213	16	on	on	ADP
iajs-3728	213	17	community	community	NOUN
iajs-3728	213	18	identification	identification	NOUN
iajs-3728	213	19	in	in	ADP
iajs-3728	213	20	complex	complex	ADJ
iajs-3728	213	21	networks	network	NOUN
iajs-3728	213	22	.	.	PUNCT
iajs-3728	214	1	j	j	PROPN
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iajs-3728	214	7	.	.	PUNCT
iajs-3728	215	1	https://doi.org/10.1088/1742-5468/2006/11/p11010	https://doi.org/10.1088/1742-5468/2006/11/p11010	PROPN
iajs-3728	215	2	24	24	NUM
iajs-3728	215	3	.	.	PUNCT
iajs-3728	216	1	cai	cai	PROPN
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iajs-3728	216	12	detection	detection	NOUN
iajs-3728	216	13	in	in	ADP
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iajs-3728	216	16	of	of	ADP
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iajs-3728	216	18	outlier	outlier	NOUN
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iajs-3728	216	20	.	.	PUNCT
iajs-3728	217	1	ann	ann	PROPN
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iajs-3728	217	4	.	.	PUNCT
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iajs-3728	218	3	.	.	PUNCT
iajs-3728	219	1	zhou	zhou	PROPN
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iajs-3728	219	6	.	.	PROPN
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iajs-3728	219	12	for	for	ADP
iajs-3728	219	13	community	community	NOUN
iajs-3728	219	14	detection	detection	NOUN
iajs-3728	219	15	:	:	PUNCT
iajs-3728	219	16	the	the	DET
iajs-3728	219	17	general	general	ADJ
iajs-3728	219	18	bipartite	bipartite	PROPN
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iajs-3728	219	20	.	.	PUNCT
iajs-3728	220	1	j	j	PROPN
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iajs-3728	220	6	.	.	PUNCT
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iajs-3728	221	2	.	.	PUNCT
iajs-3728	221	3	yan	yan	PROPN
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iajs-3728	221	16	of	of	ADP
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iajs-3728	221	18	in	in	ADP
iajs-3728	221	19	block	block	NOUN
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iajs-3728	221	21	.	.	PUNCT
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iajs-3728	222	4	intell	intell	PROPN
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iajs-3728	222	8	.	.	PUNCT
iajs-3728	223	1	27	27	NUM
iajs-3728	223	2	.	.	PUNCT
iajs-3728	224	1	jin	jin	PROPN
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iajs-3728	224	14	yu	yu	PROPN
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iajs-3728	224	17	li	li	PROPN
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iajs-3728	224	23	detection	detection	NOUN
iajs-3728	224	24	approaches	approach	NOUN
iajs-3728	224	25	:	:	PUNCT
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iajs-3728	224	27	statistical	statistical	ADJ
iajs-3728	224	28	modeling	modeling	NOUN
iajs-3728	224	29	to	to	ADP
iajs-3728	224	30	deep	deep	ADJ
iajs-3728	224	31	learning	learning	NOUN
iajs-3728	224	32	.	.	PUNCT
iajs-3728	225	1	ieee	ieee	PROPN
iajs-3728	225	2	trans	trans	PROPN
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iajs-3728	225	7	.	.	PUNCT
iajs-3728	226	1	https://doi.org/10.1109/tkde.2021.3104155	https://doi.org/10.1109/tkde.2021.3104155	PROPN
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iajs-3728	226	3	.	.	PUNCT
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iajs-3728	227	7	.	.	PROPN
iajs-3728	227	8	,	,	PUNCT
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iajs-3728	227	10	-	-	PUNCT
iajs-3728	227	11	peña	peña	PROPN
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iajs-3728	227	13	,	,	PUNCT
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iajs-3728	227	15	h.	h.	PROPN
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iajs-3728	227	18	detection	detection	NOUN
iajs-3728	227	19	algorithms	algorithm	NOUN
iajs-3728	227	20	in	in	ADP
iajs-3728	227	21	psychological	psychological	ADJ
iajs-3728	227	22	data	datum	NOUN
iajs-3728	227	23	:	:	PUNCT
iajs-3728	227	24	a	a	DET
iajs-3728	227	25	monte	monte	PROPN
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iajs-3728	227	28	.	.	PUNCT
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iajs-3728	228	2	.	.	PROPN
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iajs-3728	228	4	.	.	PUNCT
iajs-3728	229	1	ren	ren	PROPN
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iajs-3728	229	3	,	,	PUNCT
iajs-3728	229	4	shao	shao	PROPN
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iajs-3728	229	6	,	,	PUNCT
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iajs-3728	229	9	.	.	PROPN
iajs-3728	229	10	,	,	PUNCT
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iajs-3728	229	13	.	.	PROPN
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iajs-3728	229	15	and	and	CCONJ
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iajs-3728	229	18	in	in	ADP
iajs-3728	229	19	node	node	ADJ
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iajs-3728	229	21	networks	network	NOUN
iajs-3728	229	22	.	.	PUNCT
iajs-3728	230	1	ieee	ieee	PROPN
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iajs-3728	230	3	knowl	knowl	PROPN
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iajs-3728	230	5	eng	eng	PROPN
iajs-3728	230	6	2022	2022	NUM
iajs-3728	230	7	.	.	PUNCT
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iajs-3728	231	3	.	.	PUNCT
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iajs-3728	232	7	,	,	PUNCT
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iajs-3728	232	10	,	,	PUNCT
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iajs-3728	232	12	community	community	NOUN
iajs-3728	232	13	detection	detection	NOUN
iajs-3728	232	14	.	.	PUNCT
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iajs-3728	233	3	,	,	PUNCT
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iajs-3728	233	9	;	;	PUNCT
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iajs-3728	233	11	.	.	PUNCT
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iajs-3728	234	9	https://doi.org/10.1103/physreve.69.066133	https://doi.org/10.1103/physreve.69.066133	VERB
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iajs-3728	234	11	https://doi.org/10.1214/14-aos1290	https://doi.org/10.1214/14-aos1290	PROPN
iajs-3728	235	1	https://doi.org/10.1109/tkde.2021.3104155	https://doi.org/10.1109/tkde.2021.3104155	PROPN
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