21 

 

 

 

 

 

 

Interactive methods for graph exploration 
 

 

Eloïse Loubier 
 

 

I.R.I.T. (Institut de Recherche en Informatique de Toulouse), 

118, route de Narbonne, 31062 TOULOUSE Cedex 9 
 
 

Received 15 December 2010; received in revised form 25 February 2011; accepted 20 March 2012 

 
 

 

ABSTRACT: In a strategic watch context, visualization of relational data allows transformation, coding 

and visualization of great data quantities. Access to interactive, adjustable functionalities by the user 

would facilitate the domination and the precision of the analysis. From this point of view, the VisuGraph 

tool allows visualization and exploration of relational data, by the way of applicable and controllable 

methods of analysis. The main interactive VisuGraph functionalities are presented and illustrated, 

revealing their importance in graph exploration. The user is the heart of the tool; he or she fully controls 

the representation and directs the analysis according to own needs. 

 

Keywords: Grap exploration, strategic watch, visualization, business intelligence 

 

1. Introduction 
 

Complex and bulky data sources do not 

facilitate relations identification and relevant 

tendencies. For the majority of users, useful and 

exploitable information search constitutes a long 

and tiresome process. It requires many efforts 

for data processing specialists who are charged 

to treat the requests and to generate the ad hoc 

reports/ratios. In a strategic watch context, 

analysts must be able to explore large volumes 

of data in an interactive way. They should be 

able to study tendencies, to test various 

approaches and especially to isolate invaluable 

information bringing a competitive advantage.  

 

 

 

 

 

Relational data visualization allows 

transformation, coding and effective chart of 

great data volumes. This technique offers to the  

user a clear and readable representation of 

information, initially difficult of access. 

Analysis methods of relevant data allowing 

exploration of the data complete this 

representation. Thus, the base of any proposal of 

visualization of relational data tool supposes an 

interest in the three following fundamental 

aspects: 

 

 nature of the data represented, 

 way in which the components of the graph 
are exploited to transcribe these data, 

Available for free online at https://ojs.hh.se/ 

 

Journal of Intelligence Studies in Business 2 (2012) 21-31 

https://ojs.hh.se/


22 

 

 

 

 perception of these components by the user. 
 

The visualization objective is not simply limited 

to product pre-set charts, which cannot be 

changed by the user. Indeed, an important 

criterion for a good visualization tool is the 

possibility for the user to control the 

representation in order to include/understand the 

information space and to interact with the 

system. Visualization on this point is the 

concern of the field of man-machine interaction. 

In this article, the author presents a model of 

interactive visualization of relational data, 

named VisuGraph (Loubier and Dousset 2008).  

Communication with the user and the system is 

the main interest of concern in this context as a 

proposal of a tool of assistance to the analysis of 

relational data.  

This information analysis approach is based 

on visualization interfaces. It allows data 

exploration by rich charts and interaction modes 

adapted to analyst tasks. Visualization’s 

components and navigation help the analyst and 

more particularly “the watcher”, taking part in 

the technology’s development for a framework 

of economic, strategic and competitive 

intelligence. 

Under the control of an expert, who chooses 

a suitable mode of representation 

(“semiologyand esthetics” rules), relational data 

are represented in graph form. The visualization 

purpose is to give a precise idea of the data and 

their relations. The objective is to propose an 

information representation allowing 

identification, analysis and restitution of the 

strategic structures. This system makes it 

possible to detect different connections and to 

analyse at a specific time the actors of a field 

and the concepts which they use. This tool is 

supplemented by processes of interactive graph 

analysis, which make it possible for the user to 

control his or her representation. In this article, 

we insist on VisuGraph tool’s interactive 

functionalities and particularly on the targeted 

and comparative study of nodes. Initially, the 

stakes of data and in particular of cognition 

visualization are presented, as well as principal 

application scopes. 

In a second phase, the relational data 

visualization methodology proposed in the 

context of VisuGraph is exposed. The access of 

traditional analysis methods makes it possible to 

isolate particular nodes and obtain paramount 

information which enriches and facilitates the 

graphic analysis. A concrete example illustrates 

the effectiveness of such a tool for visualization.  

This example makes it possible to analyze 

complex interactions networks, the actors/fields 

and evolution by the analyses of graph structure. 

It is possible to detect the various tendencies, 

the strong signals and the weak signals. 

 

2. Stakes of the visualization of data 

 

Since about fifteen years, under the impulse of 

researchers like Card, Mackinlay and 

Shneiderman (1999), Spence (2000) or Ware 

(2000), information visualization has become a 

research orientation with gravitas. Many 

contributions tried to approach available 

represented formalizations to restore the space-

time processes (Langran 1993; Gayte et al. 

1997; Frank, Rapper and Cheylan 2001). 

Recently geographers and cartographers were 

also interested with these questions (Passover 

2007; Josselin and Fabrikant 2003).  

 

“The principle of information visualization is to 

use the power of the computer tools to represent 

effectively, from a cognitive point of view, 

abstract data which do not have necessarily 

usual physical representations. These 

techniques, which aim’ at amplifying cognition 

via perception, aim in particular to facilitate the 

discovery and the creation of ideas starting 

from masses of data difficult to apprehend from 

the quantity or the complexity of the information 

which they contain. ” (Fekete and Lecolinet 

2006). 

 

 Many techniques of information 
visualization have been proposed over the 

fifteen last years, as is shown in the work to 

identify 6 leading causes of cognition 

amplification by visualization (Card, 

Mackinlay and Shneiderman 1999): 

 

 Reduction of the cognitive resources 
mobilized by the user to process and 

analyze the data (interaction raised with the 

user, perception carried out in parallel and 

accessibility to a great quantity of 

information), 

 

 Simplification of information search (many 
data in a small space, regrouping of data for 

example by criteria), 

 

 Increasing structures detection possibilities 
(relations between significant data, 

regroupings, strategic positions and 

centrality), 

 

 “Perceptual inference” using visual 
perception (problems appear using a visual 

representation), 

 



23 

 

 

 

 Monitoring of the events (changes of 
structures, appearance or movement in the 

reasons and regroupings), 

 

 Means of handling data (interactive 
navigation). 

 

 

Thus, visualization must make it possible to 

discover, to propose explanations or to take 

decisions. These actions can be done based on 

specific reasons (clusters, tendencies and 

emergences) or on the whole of elements or on 

isolated elements. Visualization technologies 

make it possible to effectively communicate 

information via cognitive charts. This kind of 

representation facilitates the discovery of 

knowledge by the way of charts resulting from 

the analysis of a corpus (Balmisse 2005). Many 

techniques of data visualization were proposed 

to date in various applications such as clinical 

data study (Shahar and Cheng 1999), 

geographical data (MacEachren et al. 1998), 

hydrometric data (Kramer and Jozsa 1998), 

personal data (such as those contained in a 

medical file) (Liking et al. 1996) and at various 

ends such as significant tendencies research 

(Harbour and Al 2000), exploration of programs 

traces (Renieris and Reiss 1999), data analysis 

of logs (Hochheiser and Schneiderman 2002), 

temporal abstractions representation (Shahar 

and Cheng 2000) or the visualization of 

temporal association rules (Rainsford and 

Roddick 2000).  

Analysis of relational information evolution 

is mainly based on the visualization of dynamic 

graphs. Many researchers developed display 

systems of networks (Di Battista et al. 1999), by 

taking into account a cartography of 

connectivity related to the Internet, the networks 

of phone calls, the networks of quotation as well 

as the progressive visualization of the 

evolutionary fields of knowledge. 

We study the evolution of research themes, 

information visualization, according to 

Kapusova (2004), who combines aspects of 

scientific visualization, man-machine interfaces 

(human-computer interfaces), excavation of data 

(mining dated), imagery and graphs. For (Fekete 

and Lecolinet 2006) information visualization 

was detached from three related fields: the Man-

machine interaction, the analysis statistics and 

cartography, but also scientific visualization. 

Thus, the distinction must be made between the 

visualization which refers to the process which 

leads to a chart and the interactive information 

chart which milked the means of interactions 

which use information charts. The user’s role in 

the tools for data visualization is a subject of 

major concern (Grinstein 1996; Fayyad, 

Grinstein and Wierse 2002). Thus interactive 

visualization of relational data brings to the user 

an artificial substrate which transcribes a great 

amount of information. It makes function of 

support to its knowledge and its intuition to 

enable him or her to discover new relations, to 

help with decision making and to allow 

anticipation, as well as for the evolution of these 

data. In visual excavation of data, the interaction 

materializes the loop of feedback between the 

user and the visual aids (Keim and Kriegel 

1996). A majority of visualization tools offer 

the access to powerful statistical methods of 

analysis but these methods are not really 

interactive and users can only seldom direct the 

totality of the chart. 

3. Methodology 

 

VisuGraph is a data visualization tool. It is 

developed in java (Loubier and Dousset 2008). 

The list of alliances is regarded as a document 

population. Two actors represented graphically 

in the form of nodes are considered concurrent 

if they are present in the same alliance (there 

can be more than two actors per alliance). The 

whole of these co-occurrences is counted in a 

square matrix crossing, two to two, as far as the 

actors are concerned.  

The data are represented in a graph simple G 

characterized by two units: a unit V = 

{v1,v2,…,vn} whose elements are called tops, and 

a unit E = {e1,e2,…,em}, left the unit  of 

the parts with two elements of V, which are 

called edges. G will be noted G = (V, E). G is a 

graph not directed (there is no distinction 

between (u, v) and (v, u) for u and v in V) and as 

simple (there is no loop (v, v) in E and there 

exists more than one bond between two nodes).  

Recourse to visual artifices makes it possible to 

represent information as well as possible, by the 

means of a particular semiology on the level of 

tops form and colors used. In VisuGraph, data 

are represented in circle form where size is 

proportional to the value of the metric. Bonds 

are represented by segments binding two nodes, 

coded according to color. 

 

a. Interactivity on semiology  

Graphic visualization comprehension is based 

on construction rules of a symbolic system. 

Study of the signs and their significance is 

called semiology. It is also based on a codified 

use of the writings and on general aesthetic 

principles. Bertin (1970) is regarded as the 

initiator in term of cartography of information. 

He is interested in construction of visualization 

by graphic symbols.  



24 

 

 

 

Graphic semiology is based on: 

 

 significance of the drawings, 

 choice of legends, symbols, icons, 

 methodology to transmit a visual message. 
 

Semiology quality goes with the possibility for 

the user to be able to control it fully. With 

regard to the variation of color, value is a 

variation of luminous intensity of darkest to the 

most clear, or conversely. It translates an order 

relation and differences (quantitative relation).  

However, our capacity to be recognized is much 

more limited than our aptitude to be 

appreciated: 

 

 on the one hand, differential sensitivity of 
the eye to luminous energy is not directly 

proportional to the intensity of flow. The 

appreciation of the ranges is lower in clear 

colors than in the beds, 

 

 in addition, our differential chromatic 
sensitivity is not uniform and specific to 

each one. 

 

 The tops and the edges being balanced, 
initially, the color makes it possible to code 

the value of the metric of each element of 

the graphs. The tops/edges of stronger 

metrics will be colored by strong intensity 

and conversely. 

 

The user can accentuate contrasts of colors 

used. Thus, for coding by the edges color, a 

measurable rule is placed at the disposal of the 

user in order to enable him or her to attenuate or 

increase the intensity of the color. It is the same 

for labelling of the data which size and intensity 

can be regulated, thus making it possible to fully 

control the importance of this information. 

Indeed big size and strong intensity make 

visualization less readable than if they are of 

small size and homogeneous color with the 

prime coat of the representation. 

 

In the following figure, five tops are extracted 

from a total graph. The initial police force used 

here as an example is based on small size and 

low intensity, in order not to deteriorate the 

legibility of the graph. The graph of the medium 

results comes from the increase in the value of 

the rules of graduation and of the intensity of 

the police force and the bonds between them, 

but also of the size of the police force. The 

graph of right-hand side results from an increase 

even more important in the values of these rules. 

 

 

 

 

 

 

 

Figure 1: Interactivity on the semiology of the graph. 

 

b. Attraction and répulsion 
forces 

The base of any graph analysis is based on the 

clearness and the legibility of the representation. 

In the case of a no-planar graph, the number of 

edges crossings can quickly make the graph 

illegible and complicate its interpretation.  

Much work was carried with powerful 

algorithms of directed placement by the means 

of forces (FDP: “Force Directed Placement”) 

(Tutte 1963; Eades 1984; Kamada and Kawai 

1989; Fruchterman and Reingold 1991). 

 

 

 

 

 

 

 

The algorithm FDP proposed in VisuGraph is 

based on Fruchterman and Reingold’s (1991) 

work and makes it possible for the user to 

intervene on the application of the forces, by 

increasing or decreasing them by the skew of 

two scales, the attraction force or of repulsion.  

The attraction force between the tops can be 

proportional to the force of the bond between 

them. The attraction force between two tops υi 

and υj is given by: 

 

   (1) 



25 

 

 

 

The factor K is calculated according to a 

drawing surface and of the tops number. duv is 

the distance between u and v in the drawing.  

corresponds to the scale value for the 

attraction divided by two. It is used to define the 

attraction degree between two tops.  

 

K makes it possible to represent every edge in 

the representation window and not out of it. L 

represents the window length, L the width and 

nbtops corresponds to the number of visible tops 

of the graph. 

 

  

  (2) 
 

If the tops u, v are not connected by an edge 

then ƒa (u, v) = 0. Repulsion force between two 

tops u, v is defined by: 

 

   (3) 

 

  corresponds to the value of the scale 

corresponding to the repulsion, allowing to 

interact on the repulsion; it makes it possible to 

define the repulsion degree between two tops u 

and v.  

 

Thus, the higher the attraction threshold chosen 

by the user, the more the dependent tops attract 

themselves, supporting the total drawing of the 

graph structure to the detriment of inter-nodes 

relations. In the same way, the more the 

repulsion threshold is raised, the more the 

structure is widened, the non-dependent tops are 

pushed back and those united by an edge are 

more distinguished, allowing us to obtain more 

burst structure.  

The combination of the parameter setting of 

these two forces leads to a more readable 

representation, as shown in the figure 2, where 

the graph (1) is a planar and on which no 

algorithm FDP was not applied; the graph (2) is 

the result of the application of our algorithm for 

which the rules of graduation for attraction and 

the repulsion were positioned, here with median 

values (5 for each one on scales from 1 to 10). 

 

c. Transitivity  

Navigation in the initial graph is often too 

complex. In order to carry out it, it is possible to 

work on a subgraph. We start from a particular 

top selected in the complete graph and gradually 

extend the graph by transitivity. This technique 

allows, by a change of x-ray, to concentrate on a 

relevant extract resulting from targeted 

information (actor, key word and concept). 

Measurements of degree centrality and 

constraint privilege the local point of view. 

More precisely, a data is known as a power 

station if it is strongly connected to the other 

members of the graph. The concept of centrality 

makes it possible to specify the dominant 

position of an actor, or a node in the network 

(Freeman 1979).  We base our work on the 

algorithm of Floyd (1962). It is based on a 

generalization to the case of valued graphs by a 

calculation algorithm of graph transitive 

closing, discovered about simultaneously in 

France by Roy (1959) and in the United States 

by Warshall (1962).  

The transitive closing of a graph G= (X, A) 

is the minimal transitive relation containing the 

relation (X, A), it acts of a graph G*= (X, A*) 

such as (x, y) ∈ A* if and only if there exists a 
way f in G beginning by x and ending by y. The 

calculation of transitive closing makes it 

possible to answer the questions concerning the 

existence of ways between x and y in G and this 

for any couple of tops (x, y).  (X, A*) calculation 

is carried out by iteration of the basic operation 

ϕx (A) which adds the arcs (y, z), and asks is a 

predecessor of x and z one of its successors. 

More formally: 

 

 

  (4) 

Definition :   

 

For any top X,  

 (5) 

 

For any couple of top (x, y),  

 

 

  (6) 

 

Transitive closing A* is given by: 

 

 

  (7) 

 

In our contribution, the user selects a specific 

node in the graph. Other tops are masked and 

only the initially selected node remains visible. 

By the means of a scale, the change of 

transitivity is carried out. The first step indicates 

the direct neighbours of the node, who are then 

visible, like the bond with the initial node. The 

more the value of the scale is increased, the 

more the threshold of transitivity is important.   

This study of the structure of the graph and in 

particular of the topology of a particular node 

makes it possible to qualify this last and to study 



26 

 

 

 

its role within the whole of the studied 

population. If transitivity reveals many direct 

links towards other data this shows that the 

major importance of the data represented is 

revealed. Sub graph studies by transitivity make 

it possible to carry out a more pointed analysis 

and to detect the most important actors. 

However, it is interesting to compare the 

typology of several important graph actors in 

order to be able to compare them with the same 

element: 

 

 The number of bonds with direct 
neighbours (transitivity of first threshold) 

and it importance at the local level, 

 

 Importance of the basic data studied within 
the total graph. 

 

 It is necessary to preserve an image of the 
first threshold of transitivity for each 

studied top. 

 

We add to VisuGraph a functionality allowing 

preserving a precise image of the top transitivity 

at one specific moment in the form of a small 

independent window. It is located near the main 

visualization window. The comparison of 

different sub graphs structure resulting from 

transitivity makes it possible to distinguish 

remarkable elements. In the following figure, 

the studied data are authors having taken part in 

a scientific congress on the topic of strategic 

watch. In this article context, the data are used 

at the end to do illustration of interactive 

methods, not as a complete analysis of this 

congress. In order to facilitate the graph 

legibility, we reduced to the maximum the size 

of wording. Based on a global graph 

representation, we can see that stronger 

centrality data are distinguished and the sub 

graphs based on these data are extracted in order 

to be able to compare them. The higher graph 

corresponds to the total graph of the whole of 

the dataset. The interest is related to a node in 

particular which appears in the total graph 

which is strongly connected to the other tops. It 

is then interesting to calculate the transitive 

closing of this top and to study the structure of 

it. In this way, the top is isolated (3), by 

masking of the other tops. In the second time, 

using the change of graduation of the rule of 

transitivity, the direct neighbours of this top are 

obtained (4). The important number of direct 

connections of this node is remarkable, which 

means that the author is a paramount actor 

within his team and that he collaborates 

frequently with other researchers. By increasing 

the threshold of transitivity, we obtain the graph 

(5), representing the maximum threshold of 

transitivity for this top. Comparison between 

this visualization and the total graph reveals the 

similarity between the latter. 

The author initially selected can thus be 

qualified as being one of the elements at the 

origin of the total structure of the graph, i.e. a 

very important author.



27 

 

 

 

 
Figure 2: Extraction starting from the total graph of a specific top and calculation of its transitivity. 

 

d. Filtering  

The filtering concept is based on the metric 

values. It consists in preserving only the tops 

and the edges of the associated graph with the 

values higher or equal to a threshold, according 

to a value fixed by the user via a scale. This 

procedure reveals the most representative tops, 

as well as the important components of the 

structure. The visualization of the result after 

filtering can be made by masking (total) 

adjacent edges and consequently their tops, 

having a value of metric lower than the 

threshold defined by the user. This kind of 

representation extracts the elements 

representative of the graph in terms of value of 

the metric (Loubier and Dousset 2008). 

e. K-core 

The decomposition in k-core (Batagelj and 

Zaversnik, 2002) consists in identifying 

particular subsets of the graph called k-core. 

Consider a graph G = (V, E) with |V| = n tops  

 

 

 

and |E| = e edges. A k-core is defined as 

follows: 

 

Definition 

 

A subgraph H = G (C, E|C) armature by the 

subset C⊆V is a k-core or a core of order k 
if and only if  and 

H is a maximum subset with this property. 
 

The related subset is characterized by a 

“coreness” cE. It forms a cluster (a community) 

within the meaning of (Alvarez and Al, 2005). 

The k-core is obtained by recursive pruning of 

the nodes which have a smaller degree than k. 

The graph remaining contains only tops of 

degree ≥ k. K-core decomposition makes it 

possible to obtain a hierarchical partitioning of 

the tops such as the whole of coreness 1 is in top 

of the hierarchy and the maximum whole of 

coreness is at the bottom of the hierarchy. This 

partition depends on the degree of each top and 



29 

 

 

 

the degrees in the vicinity. Complexity in time 

of the algorithm of decomposition in a k-core of 

Alvarez-Hamelin et al. (2005) is O (n + m) 

where n and m are respectively the number of 

nodes and edges in the network. Applied to 

VisuGraph, the k-core is calculated starting 

from a threshold fixed by the user, via a scale. 

The more this threshold increases, the higher the 

coreness is. The obtained graph corresponds to 

decomposition in k-core, according to the 

threshold value chosen, via a scale. 

 

In the following figure, the k-core is applied for 

one k = 5, which means that only the nodes 

having at least five bonds are preserved. The 

results obtained allow: 

 To visualize the main actors having 
collaborated; 

 

 To distinguish the various teams, i.e. all the 
actors having to work more together. 

 

Thus several different communities are 

distinguished, by the means of this method. It is 

noted that the global graph contains three large 

important teams. Within each one of these 

groups, one distinguishes the major actors from 

each team, who are in the middle of the 

connections and thus the absence would divide 

the team, such as for example the nodes circled 

in the graph of the bottom of figure 2. 

 
 

Figur 3: k-core (k=5). 



30 

 

 

 

4. Conclusion 

 

In this article, we presented several interactive 

functionalities of the temporal data visualization 

tool, named VisuGraph. User point of views and 

user directives are taken into account, on the 

level of his or her needs but also on the level of 

its intervention when handling is dominant in 

the design of a tool of assistance to the analysis 

and for decision making. The tool and the 

visualization should not be fixed. The user must 

be free to be able to control fully his or her 

representation while intervening on the various 

statistical methods suggested, but also on the 

semiology suggested by the tool. Within the 

framework of this article, we insist on the 

statistical analysis functionalities suggested by 

the VisuGraph tool, such as: 

 

 The tops directed placements algorithm, 

 transitivity, 

 filtering, 

 k-core. 
 

These various methods facilitate and improve 

exploration and graph structure analysis, like the 

particular study of specific nodes. Our proposal 

is suggested by the interactivity offered for 

these traditional statistics methods. This 

interactivity between user and system makes it 

possible to control fully the chart, by means of 

scales and thus to target its analysis in order to 

obtain more precision as for the structure of the 

graph. Thus, VisuGraph tool answers the 

principal ideas proposed by (Card, Mackinlay 

and Shneiderman 1999) such as: 

 

 Cognitive resources reduction via an 
interaction raised with user,  

 

 Representation in graphic form of great 
volumes of data, 

 

 Increase of structures detection by the way 
of precise and interactive statistical 

methods, 

 

 Means of handling the data representation 
by semiology control. 

 

References 
 

Alvarez-Hamelin, J.I., Dall’Asta, L., Barrat, 

A. and Vespignani, A. 2005. k-core 

decomposition: a tool for the 

visualization of large scale networks. 

Computing Research Repository: 41, 52-

55. 

 

Balmisse, G. 2005. REFLEXIONS - 

Visualisation de l'information: quelques 

repères, retrieved 2005, 

[http://www.gillesbalmisse.com/blog/inde

x.php?2005/02/24/35-visualisation-de-

linformation-quelques-reperes] 

Batagelj, V. and Zaversnik, M. 2002. 

Generalized cores. Working paper, 

Department of theoretical computer 

science: 52. 

Bertin, J. 1970. Sémiologie graphique. Haag: 

Mouton. 

Card, S.K., Mackinlay, J.D. and 

Shneiderman, B. 1999. Readings in 

Information Visualization. San Francisco: 

Morgan Kaufmann Publishers. 

Di Battista, G., Eades P., Tamassia, R. and 

Tollis, I.G. 1999. Graph drawing - 

Algorithms for the visualization of 

graphs. Upper Saddle River: Prentice 

Hall. 

Eades, P. 1984. A Heuristic for Graph 

Drawing, Congressus Numerantium, 42: 

149-160.  

Fayyad, U., Grinstein, G. and Wierse. A. 

2002. Information visualization in data 

mining and knowledge discovery. San 

Fransisco: Morgan Kaufmann. 

Fekete, J.D. and Lecolinet, E. 2006. 

Visualisation pour les bibliothèques 

numériques. Hermès-Lavoisier. 9 (2) : 7-

11. 

Floyd, R. 1962. Algorithm 97: Shortest Path. 

Communications of the ACM, 5, June: 

345.  

Frank, A., Rapper, J. and Cheylan, J.-P. 

2001. Life and Motion of Socio-

economic Units. London: Taylor and 

Francis, page 353. 

Freeman, L.C. 1979. Centrality in Networks: 

Conceptual Clarification. Social 

Networks, 1: 215-39.  

Fruchterman, T.M.J. and Reingold, E.M. 

1991. Graph drawing by force_directed 

placement. Software – Practice and 

experience, 21: 1129-1164.  

Gayte, O., Libourel, T., Cheylan, J.P. and 

Lardon, S. 1997. Conception des 

systèmes d’information sur 

l’environnement. Paris: Hermès, 153. 

Grinstein, G. 1996. Harnessing the human in 

knowledge discovery. Proceedings f the 

2nd International Conference on 

http://www.gillesbalmisse.com/blog/index.php?2005/02/24/35-visualisation-de-linformation-quelques-reperes
http://www.gillesbalmisse.com/blog/index.php?2005/02/24/35-visualisation-de-linformation-quelques-reperes
http://www.gillesbalmisse.com/blog/index.php?2005/02/24/35-visualisation-de-linformation-quelques-reperes
http://en.wikipedia.org/wiki/Communications_of_the_ACM


31 

 

 

 

knowledge Discovery and Data Mining, 

Portland, USA: 384-385. 

Hochheiser, H. and Shneiderman, B. 2002. A 

dynamic Query Interface for Finding 

Patterns in Time Series Data. 

Proceedings of conference CHI, April 

2002, Minneapolis, USA. 

Josselin, D. and Fabrikant, S. 2003. 

Cartographie animée et interactive. Revue 

internationale de géomatique, 13 (1).  

Kamada, T. and Kawai, S. 1989. An 

Algorithm for General Drawing 

Undirected Graphs. Information 

Processing Letters, 31: 7-15.  

Kapusova, D. 2004. Visualisation de 

l'information dans le portail STAF18. 

http://tecfaseed.unige.ch/staf18/modules/

ePBLjolan/uploads/proj15/paper%20(et%

20dispositif)6.xml. Accessed 2011-11-24 

Keim D., Kriegel H. Visualization 

techniques for mining large databases: a 

comparison. IEEE Trans. On knowledge 

and Data Engineering, 1996: 923-938. 

Kramer, T. and Jozsa, J. 1998. Visualization 

and analysis of timedependent 

hydrometric data in windows 

environment. Proceedings of the 3rd 

International Conference on 

Hydroinformatics, 1998, Copenhagen, 

A.A. Balkema Publisher  

Langran, G. 1993. Time in Geographic 

Information Systems. London: Taylor & 

Francis, 189.  

Loubier, E. and Dousset, B. 2008. Temporal 

and relational data representation by 

graph morphing”. Dans : Safety and 

Reliability for managing Risk (ESREL 

2008), Hammamet, 14/02/2008-

16/02/2008, February 

ftp://ftp.irit.fr/IRIT/SIG/2008_ESREL_L

D.pdf. 

MacEachren, A.M., Bosco F.P, Haug D., 

Pickle L.W. 1998. Geographic 

Visualization: Designing Manipulable 

Maps for Exploring Temporally Varying 

Georeferenced Statistics. Proceedings of 

the IEEE Symposium on Information 

Visualization InfoVis, Research Triangle 

Park, USA, IEEE Computer Society: 87-

94.  

Rainsford, C.P. and Roddick, J.F. 2000.  

Visualisation of Temporal Association 

Rules. Proceedings of 2nd International 

Conference on Intelligent Data 

Engineering and Automated Learning 

IDEAL, Hong Kong, Chine: 91-96. 

Renieris, M. and Reiss, S.P. 1999. 

ALMOST: Exploring Program Traces. 

Workshop on New Paradigms in 

Information Visualization and 

Manipulation: 70-77. 

Shahar, Y. and Cheng, C. 1999. Intelligent 

Visualization and Exploration of Time-

Oriented Clinical Data. Topics in Health 

Information Management, 20 (2): 15-31. 

Shahar, Y. and Cheng, C. 2000. Model-

Based Visualization of Temporal 

Abstractions. Computational Intelligence, 

16 (2): 279-306. 

Spence, R. 2000. Information Visualization. 

New York: ACM Press.  

Tutte, W.J. 1963. How to Draw a graph. 

Proceeding London Maths, plowshare, 3 

(13): 743-768.  

Ware, C. 2000. Information Visualization, 

perception for design. San Fransisco: 

Morgan Kaufmann. 

Warshall, S. 1962. A theorem on Boolean 

matrices. Journal of the ACM, 9, January: 

11–12. 

 

 
 

http://geo.e-revues.com/
http://tecfaseed.unige.ch/staf18/modules/ePBLjolan/uploads/proj15/paper%20(et%20dispositif)6.xml
http://tecfaseed.unige.ch/staf18/modules/ePBLjolan/uploads/proj15/paper%20(et%20dispositif)6.xml
http://tecfaseed.unige.ch/staf18/modules/ePBLjolan/uploads/proj15/paper%20(et%20dispositif)6.xml
http://www.irit.fr/publications.php3?code=3983&nom=Loubier%20Elo%C3%AFse
http://www.irit.fr/publications.php3?code=114&nom=Dousset%20Bernard
ftp://ftp.irit.fr/IRIT/SIG/2008_ESREL_LD.pdf
ftp://ftp.irit.fr/IRIT/SIG/2008_ESREL_LD.pdf
http://en.wikipedia.org/wiki/Journal_of_the_ACM