Maps of Information Flow Reveal Community Structure In Complex Networks

Abstract

To comprehend the multipartite organization of large-scale biological and social systems, we introduce a new information theoretic approach to reveal 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. Biological and social systems are differentiated, multipartite, integrated, and dynamic. Data about these systems, now available on unprecedented scales, are often schematized as networks. Such abstractions are powerful (1, 2), but even as abstractions they remain highly complex. It is therefore helpful to decompose the myriad nodes and links into modules that