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      Maps of random walks on complex networks reveal community structure

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          Abstract

          To comprehend the multipartite organization of large-scale biological and social systems, we introduce a new information theoretic approach that reveals community structure in weighted and directed networks. The method decomposes a network into modules by optimally compressing a description of information flows on the network. 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 more than 6000 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.

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          Author and article information

          Journal
          04 July 2007
          2007-11-12
          Article
          10.1073/pnas.0706851105
          2234100
          18216267
          0707.0609
          Custom metadata
          PNAS 105, 1118-1123 (2008)
          7 pages and 4 figures plus supporting material. For associated source code, see http://www.tp.umu.se/~rosvall/
          physics.soc-ph cond-mat.dis-nn physics.data-an

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