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Similar to the way Google’s search engine ranks pages within the vast world wide web and matches them to your search query, Meta ranks papers, journals, authors and other biomedical concepts, then matches them to your feeds. Both platforms use a mathematical concept called “eigenvector centrality,” which measures the relative importance of the individual parts, or nodes, within a network. The Eigenfactor® algorithm was originally developed to rank journals, but it can be applied to authors, institutions or any other entity that can be extracted from a scientific paper.

Meta’s predictive algorithm combines many of these entities to determine the most relevant results. For recently published papers, it predicts an article’s impact 3 years from the publication date.

Top papers in your personalized feeds are organized such that recently published papers with the highest rank are displayed toward the top of the feed.

For more details about the Eigenfactor® algorithm, visit


Eigenfactor® is a registered trademark of the University of Washington and used under license. Use of this trademark is not an endorsement by UW of any particular product, service, or enterprise.