An information-theoretic framework for resolving community structure in complex networks.
To understand the structure of a large-scale biological, social, or technological network, it can be helpful to decompose the network into smaller subunits or modules. In this article, we develop an information-theoretic foundation for the concept of modularity in networks. We identify the modules of which the network is composed by finding an optimal compression of its topology, capitalizing on regularities in its structure. We explain the advantages of this approach and illustrate them by partitioning a number of real-world and model networks.
Rosvall
Martin
M
Department of Biology, University of Washington, Seattle, WA 98195-1800, USA. rosvall@u.washington.edu
Bergstrom
Carl T
CT
D019295:Computational Biology; D008954:Models, Biological; D016571:Neural Networks, Computer