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Edited by Brian Skyrms, University of California, Irvine, CA, and approved December 10, 2007 (received for review July 21, 2007)
Abstract
To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network—including physics, chemistry, molecular biology, and medicine—information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.
Footnotes
- †To whom correspondence should be addressed. E-mail: rosvall{at}u.washington.edu
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Author contributions: M.R. and C.T.B. designed research, performed research, and wrote the paper.
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The authors declare no conflict of interest.
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This article is a PNAS Direct Submission.
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This article contains supporting information online at www.pnas.org/cgi/content/full/0706851105/DC1.
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Freely available online through the PNAS open access option.
- © 2008 by The National Academy of Sciences of the USA
Maps of random walks on complex networks reveal community structure
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