Journal
EPL
Volume 103, Issue 2, Pages -Publisher
IOP PUBLISHING LTD
DOI: 10.1209/0295-5075/103/28003
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Funding
- National Science Foundation [DMS-1107796]
- Air Force Office of Scientific Research (AFOSR)
- Defense Advanced Research Projects Agency (DARPA) [FA9550-12-1-0432]
- Direct For Mathematical & Physical Scien [1107796] Funding Source: National Science Foundation
- Division Of Mathematical Sciences [1107796] Funding Source: National Science Foundation
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Many methods have been proposed for community detection in networks. Some of the most promising are methods based on statistical inference, which rest on solid mathematical foundations and return excellent results in practice. In this paper we show that two of the most widely used inference methods can be mapped directly onto versions of the standard minimum-cut graph partitioning problem, which allows us to apply any of the many well-understood partitioning algorithms to the solution of community detection problems. We illustrate the approach by adapting the Laplacian spectral partitioning method to perform community inference, testing the resulting algorithm on a range of examples, including computer-generated and real-world networks. Both the quality of the results and the running time rival the best previous methods. Copyright (C) EPLA, 2013
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