%0 Journal Article %1 newman2004finding %A Newman, M E %A Girvan, M %D 2004 %J Phys Rev E Stat Nonlin Soft Matter Phys %K detection modularity network %N 2 %P 026113.1-15 %T Finding and evaluating community structure in networks %U http://www.ncbi.nlm.nih.gov/pubmed/14995526 %V 69 %X 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 any 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.