Finding and evaluating community structure in networks Export

@article{citeulike:154, 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.}, archivePrefix = {arXiv}, author = {Newman, M. E. J. and Girvan, M.}, citeulike-article-id = {154}, citeulike-linkout-0 = {http://arxiv.org/abs/cond-mat/0308217}, citeulike-linkout-1 = {http://arxiv.org/pdf/cond-mat/0308217}, citeulike-linkout-2 = {http://dx.doi.org/10.1103/PhysRevE.69.026113}, day = {11}, doi = {10.1103/PhysRevE.69.026113}, eprint = {cond-mat/0308217}, keywords = {centrality, network}, month = {Aug}, posted-at = {2008-07-15 20:08:15}, priority = {2}, title = {Finding and evaluating community structure in networks}, url = {http://dx.doi.org/10.1103/PhysRevE.69.026113}, year = {2003} }

TY - JOUR ID - citeulike:154 L3 - citeulike-article-id:154 N2 - 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. TI - Finding and evaluating community structure in networks KW - centrality KW - network AU - Newman, MEJ AU - Girvan, M PY - 2003/Aug/11/ UR - http://dx.doi.org/10.1103/PhysRevE.69.026113 ER -