2003/08/11 by M. E. J. Newman, M. Girvan · 3 citations
Physics and Astronomy · #cond-mat.stat-mech #cond-mat.dis-nn
paper · pdf · doi:10.1103/physreve.69.026113
published as Phys. Rev. E 69, 026113 (2004) · 16 pages, 13 figures
arxiv created 2003/08/11 · arxiv updated 2009/12/01
We propose and study a set of algorithms for discovering community structure in networks -- natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.