Eigenfactor measures journal influence

As I have mentioned in a previous blog post, Eckerd College does not subscribe to Journal Citation Reports (JCR) from Thomson ISI. JCR give the impact factors for journals indexed by Thomson ISI. Because we are looking at some of our indices and databases, I wanted a way to recognize commonly used journals in certain disciplines. I discovered a recent article by an associate professor of biology at the University of Washington:

Bergstrom, C. G. (2007). Eigenfactor: Measuring the value and prestige of scholarly journals. College & Research Libraries News 68(5),314-316.

Bergstrom (2007) discusses an algorithm for ranking journal that is similar to the manner in which Google ranks Web pages. Citations stand in for hyperlinks in tracing influential journals. Bergstrom and colleagues call the ranking, Eigenfactor, and make it freely available at www.eigenfactor.org. The Eigenfactor looks at citations over a five-year period, and measures

the importance of a citation by the influence of the citing journal divided by the total number of citations appearing in that journal (Bergstrom, 2007, p.314).

According to Bergstrom (2007), the Eigenfactor indicates the amount of use a journal is getting by scholars. It is unclear whether scholars or researchers are thought to be faculty only, faculty and graduate students, or include undergraduate students also.
A second measurement calculated is Article Influence, which more closely correlates to ISI impact factor. The Article Influence

is proportional to the Eigenfactor divided by the number of articles (Bergstrom, 2007, p. 315).

Finally, the application compares actual journal prices with their influence.

At Eigenfactor.com, an additional feature allows interactive mapping of the scientific flow of information. The image below is a static map of the sciences in 2004. To create this map, 6,362,307 citations were partitioned into 82 modules. For more information on mapping, see the paper: M. Rosvall and C. T. Bergstrom (2007) Maps of information flow reveal community structure in complex networks arXiv physics.soc-ph/0707.0609v1