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Finding and evaluating community structure in networks | | M. E. J. Newman
; M. Girvan
; | | Date: |
11 Aug 2003 | | Journal: | Phys. Rev. E 69, 026113 (2004) | | Subject: | Statistical Mechanics; Disordered Systems and Neural Networks | cond-mat.stat-mech cond-mat.dis-nn | | 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. | | Source: | arXiv, cond-mat/0308217 | | Other source: | [GID 683208] pmid14995526 | | Services: | Forum | Review | PDF | Favorites | |
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