may07b.indd


scholarly communication 
Carl Bergstrom 

Eigenfactor
Measuring the value and prestige of scholarly journals 

In 1927, two chemists at Pomona College published an article in Science, proposing 
that librarians could use data about citation 
rates to select appropriate journals for a 
small library collection.1 This idea has had 
an enormous influence on the structure and 
practice of scientific communication. Today, 
citation analysis is ubiquitous in evaluating 
papers, researchers, journals, departments, 
and fields. Not only do librarians use citation 
data in selecting journal subscriptions, but 
researchers use them when deciding where 
to submit their manuscripts, funding bodies 
in evaluating grant proposals, and tenure 
committees in deciding tenure cases.2 But 
as the influence of citation data has grown, 
so has criticism of its use. Much of this criti­
cism is justified; when evaluating individual 
papers or researchers, there is clearly no 
substitute for reading and understanding the 
work. However, some questions—such as 
bibliometric analysis of the relative infl uence 
of the full contents of a journal—can only 
be answered by a large­scale quantitative 
approach. For these questions, citation data 
can be useful, and we should make the best 
possible use of it. 

The scientific literature forms a network 
of scholarly articles, connected by citations.3 

Each connection in this network—that is, 
each citation—reflects the assessment of an 
individual scholar regarding which papers are 
interesting and relevant to his or her work. 
Thus contained within the vast network of 
scholarly citations is the collective wisdom 
of hundreds of thousands of authors. My col­
leagues4 and I have developed a way to use 
the network structure of citations to improve 

on simple citation counts in measuring the 
scientifi c influence of academic publications. 
At our Web site www.eigenfactor.org, we 
report these measures for the nearly 8,000 
publications indexed by Thompson Scientif­
ic’s Journal Citation Reports (JCR) as well as 
for the approximately 110,000 other journals, 
books, newspapers, and other reference items 
that are referred to by these publications. 

How it works 
Our approach is to rank journals much as 
Google ranks Web pages. While Google 
uses the network of hyperlinks on the Web, 
we use citations in the academic literature 
as tallied by JCR. By this approach, we aim 
to identity the most “infl uential” journals, 
where a journal is considered to be infl u­
ential if it is cited often by other infl uential 
journals. While this might sound hopelessly 
circular, it is not: we can iteratively calcu­
late the importance of each journal in the 
citation network by a simple mathematical 
algorithm. 

This iterative ranking scheme, which we 
call Eigenfactor, accounts for the fact that a 
single citation from a high­quality journal 
may be more valuable than multiple citations 
from peripheral publications. We measure the 
importance of a citation by the infl uence of 
the citing journal divided by the total number 
of citations appearing in that journal. This 
corrects for differences across disciplines and 

Carl Bergstrom is associate professor in the department 
of biology at the University of Washington, e-mail: 
cbergst@u.washington.edu 
© 2007 Carl Bergstrom 

314C&RL News May 2007

mailto:cbergst@u.washington.edu
http:www.eigenfactor.org


journals in the propensity to cite other papers. 
For example, a citation from a review article 
that has cursory references to large numbers 
of papers counts for less than a citation from a 
research article that cites only papers that are 
essentially related to its own argument. 

Eigenfactor measures the total infl uence 
of a journal on the scholarly literature or, 
comparably, the total value provided by all 
of the articles published in that journal in a 
year. This seems the appropriate metric for 
making subscription decisions. All else equal, 
larger journals will have more citations and 
larger Eigenfactor scores and will be visited 
more often by researchers. If, on the other 
hand, one wants to estimate the importance 
of an article by the company it keeps, the 
size of the journal in which it is published 
is not relevant. Instead one would want to 
measure the average influence of articles ap­
pearing in the same journal. The measure that 
we use as the Article Influence for a journal 
is proportional to the Eigenfactor divided 
by the number of articles. This measure is 
more directly comparable to ISI’s familiar 
Impact Factor. 

In the sidebar below, we list the top ten 
nonreview journals in the sciences, ranked 
by the Article Infl uence. 

The Article Influence is scaled with respect 
to a mean of 1.00; thus Science has more than 
17 times the influence of the mean journal in 
Thompson Scientific’s ISI database. 

In addition to ranking the scholarly jour­
nals listed by ISI, we evaluate the importance 
of other reference materials to 

rough estimate of how often a journal will be 
used by scholars. The Eigenfactor algorithm 
corresponds to a simple model of research 
in which readers follow citations as they 
move from journal to journal. The algorithm 
effectively calculates the trajectory of a hy­
pothetical “random researcher” who behaves 
as follows. Our random researcher begins by 
going to the library and selecting a journal 
article at random. After reading the article, she 
selects at random one of the citations from the 
article. She then proceeds to the cited work 
and reads a random article there. She selects 
a new citation from this article, and follows 
that citation to her next journal volume. The 
researcher does this ad infinitum. Since we 
lack the time to carry out this experiment 
in practice, Eigenfactor uses mathematics to 
simulate this process. 

Because our random researcher moves 
among journals according the citation network 
that connects them, the frequency with which 
she visits each journal gives us a measure of 
that journal’s importance within network of 
academic citations. Moreover, if real research­
ers find a sizable fraction of the articles that 
they read by following citation chains, the 
amount of time that our random researcher 
spends with each journal may give us a rea­
sonable estimate of the amount of time that 
real researchers spend with each journal. 

As librarians work to meet increasing sub­
scription prices with increasingly constrained 
subscription budgets, powerful measures of 
journal influence and journal value may use­

these scholarly journals. For Top ten nonreview journals in the sciences
example, we can see which  Article 
newspapers have the great­ Journal Influence Eigenfactor 
est influence on the scholarly 1. Science 17.44 2.16 
literature: New York Times, 2. Cell 16.95 0.78 
The Guardian, Wall Street 3. Nature 15.88 2.32 
Jour nal, Washington Post, 4. Nature Immunology 14.54 0.19 
London Times, Miami Herald, 5. Nature Genetics 14.28 0.42 
Financial Times, Le Monde, 6. New England J. Medicine 13.88 0.72 
Boston Globe, and Los Angeles 7. Developmental Cell

8. Quarterly J. EconomicsTimes. 
9. Nature MedicineWe can view the Eigenfac­
10. Genes & Development

tor score of a journal as a 

13.24 0.10 
12.75 0.07 
12.69 0.29 
11.06 0.43 

May 2007  315 C&RL News 



fully supplement expert opinion and other 
sources of information in making diffi cult 
decisions about journal holdings. Our aim 
with the Eigenfactor project is to provide such 
a resource to the library community. 

Notes 
1. P. L. K. Gross and E. M. Gross, “College 

Libraries and Chemical Education,” Science 
66 (1927): 385­389. 

2. R. Monastersky, “The number that’s 
devouring science,” Chronicle of Higher 
Education 52 (2005) A12. 

3. D. J. de Solla Price, “Networks of Scien­
tifi c Papers,” Science 149 (1965): 510­515 

4. Ben Althouse, Martin Rosvall, and Jevin 
West at the University of Washington, and 
Ted Bergstrom at the University of California­ 
Santa Barbara. 

5. www.eigenfactor.org/methods.pdf; see 
also previous iterative algorithms proposed by 
Pinski and Narin 1978;Liebowitz and Palmer 
1984 Kalaitzidakis, et al. 2003; Palacios­Huerta 
2004; Kodryzcky 2005; Bollen 2006] 

References 
J. Bollen, M. A. Rodriguez and H. Van de 

Sompel. 2006. Journal Status. Scientometrics 
69: 669­687. 

P. Kalaitzidakis and T. Stegnos and T. 
P. Mamuneas. 2003. Rankings of academic 
journals and institutions in economics. Jour­
nal of the European Economic Association 
1: 1346­1366. 

Y. K. Kodrzycki and P. D. Yu. 2005. New 
approaches to ranking economic journals. 
Federal Reserve Bank of Boston Working 
Paper: 5­12. 

S. J. Liebowitz and J. P. Palmer. 1984. Assess­
ing the relative impacts of economics journals. 
Journal of Economic Literature 22: 77­88. 

I. Palacios­Huerta and O. Volij. 2004. The 
measurement of intellectual infl uence. Econo­
metrica 72: 963­977. 

G. Pinski and F. Narin. 1976. Citation In­
fluence for Journal Aggregates of Scientifi c 
Publications: Theory, with Application to the 
Literature of Physics. Information Processing 
and Management 12: 297­312. 

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316C&RL News May 2007

www.eigenfactor.org/methods.pdf