Eigenfactor.org measures and visualizes journal impact

December 19th, 2008

eigenfactor.org is a fascinating site that is exploring new ways to measure and visualize the importance or journals to scientific communities. The site is a result of work by the Bergstrom lab in the Department of Biology at the University of Washington. The project defines two metrics for scientific journals based on a page-rank like algorithm applied to citation graphs.

“A journal’s Eigenfactor score is our measure of the journal’s total importance to the scientific community. With all else equal, a journal’s Eigenfactor score doubles when it doubles in size. Thus a very large journal such as the Journal of Biological Chemistry which publishes more than 6,000 articles annually, will have extremely high Eigenfactor scores simply based upon its size. Eigenfactor scores are scaled so that the sum of the Eigenfactor scores of all journals listed in Thomson’s Journal Citation Reports (JCR) is 100.

A journal’s Article Influence score is a measure of the average influence of each of its articles over the first five years after publication. Article Influence measures the average influence, per article, of the papers in a journal. As such, it is comparable to Thomson Scientific’s widely-used Impact Factor. Article Influence scores are normalized so that the mean article in the entire Thomson Journal Citation Reports (JCR) database has an article influence of 1.00.”

For example, here are the ISI-indexed journals in the AI subject category ranked by the Article Influence score for 2006.

The site makes good use of GoogleDoc’s motion charts to visualize the changes of metrics for top journals in a subject area. You can also interactively explore maps that show the influence of different subject categories on one another as estimated from journal citations.

Map of Science

The details of the approach and algorithms are available in various papers by Bergstrom and his colleagues, such as

M. Rosvall and C. T. Bergstrom, Maps of random walks on complex networks reveal community structure, Proceedings of the National Academy of Sciences USA. 105:1118-1123. Also arXiv physics.soc-ph/0707.0609v3 [PDF]

(spotted on Steve Hsu’s blog)