Community extraction for social networks
Analysis of networks and in particular discovering communities within networks has been a focus of recent work in several fields and has diverse applications. Most community detection methods focus on partitioning the entire network into communities, with the expectation of many ties within communit...
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Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 108; no. 18; pp. 7321 - 7326 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
United States
National Academy of Sciences
03.05.2011
National Acad Sciences |
Subjects | |
Online Access | Get full text |
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Summary: | Analysis of networks and in particular discovering communities within networks has been a focus of recent work in several fields and has diverse applications. Most community detection methods focus on partitioning the entire network into communities, with the expectation of many ties within communities and few ties between. However, many networks contain nodes that do not fit in with any of the communities, and forcing every node into a community can distort results. Here we propose a new framework that extracts one community at a time, allowing for arbitrary structure in the remainder of the network, which can include weakly connected nodes. The main idea is that the strength of a community should depend on ties between its members and ties to the outside world, but not on ties between nonmembers. The proposed extraction criterion has a natural probabilistic interpretation in a wide class of models and performs well on simulated and real networks. For the case of the block model, we establish asymptotic consistency of estimated node labels and propose a hypothesis test for determining the number of communities. |
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Bibliography: | http://dx.doi.org/10.1073/pnas.1006642108 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Author contributions: Y.Z., E.L., and J.Z. designed research; Y.Z., E.L., and J.Z. performed research; Y.Z. analyzed data; and Y.Z., E.L., and J.Z. wrote the paper. Edited by Stephen E. Fienberg, Carnegie Mellon University, Pittsburgh, PA, and approved March 15, 2011 (received for review May 17, 2010) |
ISSN: | 0027-8424 1091-6490 |
DOI: | 10.1073/pnas.1006642108 |