%0 Journal Article %1 newman2004finding %A Newman, M.E.J. %A Girvan, M. %D 2004 %J Physical Review E %K clustering community detection girvan gn graph modularity network newman structure %P 026113 %R 10.1103/PhysRevE.69.026113 %T Finding and evaluating community structure in networks %U http://arxiv.org/abs/cond-mat/0308217 %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 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.

@article{newman2004finding, abstract = {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. }, added-at = {2006-05-15T16:42:39.000+0200}, author = {Newman, M.E.J. and Girvan, M.}, biburl = {https://www.bibsonomy.org/bibtex/25581d4204604967a209dcc712ac391af/jaeschke}, doi = {10.1103/PhysRevE.69.026113}, interhash = {b9145040e35ccb4d2a0ce18105e64ff4}, intrahash = {5581d4204604967a209dcc712ac391af}, journal = {Physical Review E}, keywords = {clustering community detection girvan gn graph modularity network newman structure}, pages = 026113, timestamp = {2018-06-04T10:09:26.000+0200}, title = {Finding and evaluating community structure in networks}, url = {http://arxiv.org/abs/cond-mat/0308217}, volume = 69, year = 2004 }