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To comprehend the multipartite organization of large-scale biological andsocial systems, we introduce a new information theoretic approach that revealscommunity structure in weighted and directed networks. The method decomposes anetwork into modules by optimally compressing a description of informationflows on the network. The result is a map that both simplifies and highlightsthe regularities in the structure and their relationships. We illustrate themethod by making a map of scientific communication as captured in the citationpatterns of more than 6000 journals. We discover a multicentric organizationwith fields that vary dramatically in size and degree of integration into thenetwork of science. Along the backbone of the network -- including physics,chemistry, molecular biology, and medicine -- information flowsbidirectionally, but the map reveals a directional pattern of citation from theapplied fields to the basic sciences.

Maps of random walks on complex networks reveal community structure