Maps of random walks on complex networks reveal community structure.
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.
Rosvall
Martin
M
Department of Biology, University of Washington, Seattle, WA 98195-1800, USA. rosvall@u.washington.edu
Bergstrom
Carl T
CT
D015706:Bibliometrics; D035843:Biomedical Research / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data; D007257:Information Theory; D008390:Markov Chains; D010506:Periodicals as Topic; D012586:Science / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data