The scholarly literature forms a vast network of academic papers connected to one another by citations in bibliographies and footnotes [1]. The structure of this network reflects millions of decisions by individual scholars about which papers are important and relevant to their own work. Therefore within the structure of this network is a wealth of information about the relative influence of individual journals, and also about the patterns of relations among academic disciplines. Our aim at eigenfactor.org is develop ways of extracting this information.
Borrowing methods from network theory, eigenfactor.org ranks
the influence of journals much as Google’s PageRank algorithm
ranks the influence of web pages [2]. By this approach, journals
are considered to be influential if they are cited often by
other influential journals. Iterative ranking schemes of this
type, known as eigenvector centrality methods [3], are notoriously
sensitive to “dangling nodes” and “dangling
clusters”: nodes or groups of nodes which link seldom
if at all to other parts of the network. Eigenfactor algorithm modifies
the basic eigenvector centrality algorithm to overcome these
problems and to better handle certain peculiarities of journal
citation data.
The Eigenfactor® score of a journal is an estimate of the percentage of time that library users spend with that journal. The Eigenfactor algorithm corresponds to a simple model of research in which readers follow chains of citations as they move from journal to journal. Imagine that a researcher goes to the library and selects a journal article at random. After reading the article, the researcher selects at random one of the citations from the article. She then proceeds to the journal that was cited, reads a random article there, and selects a citation to direct her to her next journal volume. The researcher does this ad infinitum.
The amount of time that the researcher spends with each journal gives us a measure of that journal’s importance within network of academic citations. Moreover, if real researchers 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 gives us an estimate of the amount of time that real researchers spend with each journal. While we cannot carry out this experiment in practice, we can use mathematics to simulate this process.
In addition to providing direct estimates of how often journals are likely to be used, this approach offers a number of advantages. As mentioned above, the Eigenfactor ranking system accounts for difference in prestige among citing journals, such that citations from Nature or Cell are valued highly relative to citations from thirdtier journals with narrower readership. The Eigenfactor score also adjusts for differences in citation patterns among disciplines. We can see why by looking at our example of the model researcher. Whether a journal cites 10 other journals or 100, the researcher will follow only one of those links. This is like a normalized voting system in which one can vote once with one’s full vote, ten times with each vote carrying weight 1/10th, or 100 times with each vote carrying weight 1/100th . Either way, one’s choices carry the weight of a single vote.
Further detailed information on our methods is available in PDF format. Pseudocode is available in PDF format, and complete source code in the programming language Mathematica is available in PDF format.
The modified eigenvector centrality algorithm used to rank journals at Eigenfactor.org expands upon a thirtyyear tradition of using iterative methods to quantify the influence of scholarly publications. The most important predecessors to our work include references [49] below.
Networks of Scientific Papers 


Science 169:510515 (1965) [PDF] 


The Anatomy of a LargeScale Hypertextual Web Search Engine 


WWW7 / Computer Networks 30 (17): 107117 (1998) [PDF] 


Factoring and weighting approaches to clique identification 


Journal of Mathematical Sociology, 2 : 113120. (1972) 


Citation Influence for Journal Aggregates of Scientific Publications: Theory, with Application to the Literature of Physics 

Information Processing and Management 12:297326, 1976 



5. S. J. Liebowitz and J. P. Palmer 
Assessing the relative impacts of economics journals 


6. P. Kalaitzidakis and T. Stegnos and T. P. Mamuneas 
Rankings of academic journals and institutions in
economics 


7. I. PalaciosHuerta and O. Volij 
The measurement of intellectual influence 


8. Y. K. Kodrzycki and 
New Approaches To Ranking Economics Journals 


9. J. Bollen and M. A. Rodriguez 
Journal Status 