Modularity maximization in networks by variable neighborhood search

Finding communities, or clusters, in networks, or graphs, has been the subject of intense studies in the last ten years. The most used criterion for that purpose, despite some recent criticism, is modularity maximization, proposed by Newman and Girvan. It consists in maximizing the sum for all clust...

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Bibliographic Details
Published inContemporary mathematics - American Mathematical Society Vol. 588; pp. 113 - 127
Main Authors Aloise, Daniel, Caporossi, Gilles, Hansen, Pierre, Liberti, Leo, Perron, Sylvain, Ruiz, Manuel
Format Book Chapter
LanguageEnglish
Published Providence, Rhode Island American Mathematical Society 01.01.2013
SeriesContemporary Mathematics
Subjects
community
Modularity
clustering
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ISBN9780821890387
0821890387
ISSN0271-4132
1098-3627
1098-3627
DOI10.1090/conm/588/11705

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Summary:Finding communities, or clusters, in networks, or graphs, has been the subject of intense studies in the last ten years. The most used criterion for that purpose, despite some recent criticism, is modularity maximization, proposed by Newman and Girvan. It consists in maximizing the sum for all clusters of the number of inner edges minus the expected number of inner edges assuming the same distribution of degrees. Numerous heuristics, as well as a few exact algorithms have been proposed to maximize modularity. We apply the Variable Neighborhood Search metaheuristic to that problem. Computational results are reported for the instances of the
ISBN:9780821890387
0821890387
ISSN:0271-4132
1098-3627
1098-3627
DOI:10.1090/conm/588/11705