Maps of random walks on complex networks reveal community structure

Simple item page
dc.creatorRosvall, M.
dc.creatorBergstrom, C. T.
dc.date2007-07-04
dc.date2007-11-12
dc.date.accessioned2026-07-07T09:20:04Z
dc.date.available2026-07-07T09:20:04Z
dc.descriptionTo 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.
dc.description7 pages and 4 figures plus supporting material. For associated source code, see http://www.tp.umu.se/~rosvall/
dc.identifierhttps://arxiv.org/abs/0707.0609
dc.identifierhttp://arxiv.org/abs/0707.0609
dc.identifierPNAS 105, 1118-1123 (2008)
dc.identifierdoi:10.1073/pnas.0706851105
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/154611
dc.subjectPhysics and Society
dc.subjectDisordered Systems and Neural Networks
dc.subjectData Analysis, Statistics and Probability
dc.titleMaps of random walks on complex networks reveal community structure
dc.typetext

Files

Collections

arXiv