2007/07/31 by Martin Rosvall, M. Rosvall, Carl T. Bergstrom +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Gene Regulatory Network Analysis #cond-mat.dis-nn #physics.data-an #physics.soc-ph
paper · pdf · doi:10.1073/pnas.0706851105
published as PNAS 105, 1118-1123 (2008) · 7 pages and 4 figures plus supporting material. For associated source code, see http://www.tp.umu.se/~rosvall/
arxiv created 2007/11/12 · openalex publication_date 2008/01/23 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02
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.