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 third-tier 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 thirty-year tradition of using iterative methods to quantify the influence of scholarly publications. The most important predecessors to our work include references [4-9] below.
Networks of Scientific Papers |
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Science 169:510-515 (1965) [PDF] |
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The Anatomy of a Large-Scale Hypertextual Web Search Engine |
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WWW7 / Computer Networks 30 (1-7): 107-117 (1998) [PDF] |
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Factoring and weighting approaches to clique identification |
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Journal of Mathematical Sociology, 2 : 113-120. (1972) |
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Citation Influence for Journal Aggregates of Scientific Publications: Theory, with Application to the Literature of Physics |
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Information Processing and Management 12:297-326, 1976 |
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5. S. J. Liebowitz and J. P. Palmer |
Assessing the relative impacts of economics journals |
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6. P. Kalaitzidakis and T. Stegnos and T. P. Mamuneas |
Rankings of academic journals and institutions in
economics |
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7. I. Palacios-Huerta and O. Volij |
The measurement of intellectual influence |
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8. Y. K. Kodrzycki and |
New Approaches To Ranking Economics Journals |
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9. J. Bollen and M. A. Rodriguez |
Journal Status |