Bloom: a stochastic growth-based fast method of community detection in networks

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dc.contributor.author Schumm, Phillip
dc.contributor.author Scoglio, Caterina M.
dc.date.accessioned 2012-10-17T14:22:43Z
dc.date.available 2012-10-17T14:22:43Z
dc.date.issued 2012-10-17
dc.identifier.uri http://hdl.handle.net/2097/14857
dc.description.abstract Networks are characterized by a variety of topological features and dynamics. Classifying nodes into communities, community structure, is important when exploring networks. This paper explores the community detection metric called modularity. The theoretical definitions of modularity are connected with intuitive insights into the compositions of communities. Local modularity costs/benefits are explored and an efficient stochastic algorithm, Bloom, is introduced, based on growing communities using local improvement measures. Three extensions of Bloom are presented that build upon the basic version. A numerical analysis compares Bloom with the popular fast-greedy algorithm and demonstrates the successful performance of the three modifications of Bloom. en_US
dc.relation.uri http://www.sciencedirect.com/science/article/pii/S1877750312000269 en_US
dc.subject Modularity en_US
dc.subject Community detection en_US
dc.subject Network en_US
dc.subject Greedy en_US
dc.subject Growth-based en_US
dc.subject Complex network en_US
dc.title Bloom: a stochastic growth-based fast method of community detection in networks en_US
dc.type Article (author version) en_US
dc.date.published 2012 en_US
dc.citation.doi doi:10.1016/j.jocs.2012.03.006 en_US
dc.citation.epage 366 en_US
dc.citation.issue 5 en_US
dc.citation.jtitle Journal of Computational Science en_US
dc.citation.spage 356 en_US
dc.citation.volume 3 en_US
dc.contributor.authoreid pbschumm en_US
dc.contributor.authoreid caterina en_US

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