Please use this identifier to cite or link to this item: https://doi.org/10.1209/0295-5075/101/48001
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dc.titleA spectral algorithm of community identification
dc.contributor.authorGong, X.
dc.contributor.authorLi, K.
dc.contributor.authorLi, M.
dc.contributor.authorLai, C.-H.
dc.date.accessioned2014-05-19T02:49:43Z
dc.date.available2014-05-19T02:49:43Z
dc.date.issued2013-02
dc.identifier.citationGong, X., Li, K., Li, M., Lai, C.-H. (2013-02). A spectral algorithm of community identification. EPL 101 (4) : -. ScholarBank@NUS Repository. https://doi.org/10.1209/0295-5075/101/48001
dc.identifier.issn02955075
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/52763
dc.description.abstractA novel spectral algorithm utilizing multiple eigenvectors is proposed to identify the communities in networks based on the modularity Q. We investigate the reduced modularity on low-rank approximations of the original modularity matrix consisting of leading eigenvectors. By exploiting the rotational invariance of the reduced modularity, near-optimal partitions of the network can be found. This approach generalizes the conventional spectral network partitioning algorithms which usually use only one eigenvector, and promises better results because more spectral information is used. The algorithm shows excellent performance on various real-world and computer-generated benchmark networks, and outperforms the most known community detection methods. Copyright © EPLA, 2013.
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentPHYSICS
dc.contributor.departmentTEMASEK LABORATORIES
dc.description.doi10.1209/0295-5075/101/48001
dc.description.sourcetitleEPL
dc.description.volume101
dc.description.issue4
dc.description.page-
dc.identifier.isiut000315999100030
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