Abstract：
:The characterization of network community structure has profound implications in several scientific areas. Therefore, testing the algorithms developed to establish the optimal division of a network into communities is a fundamental problem in the field. We performed here a highly detailed evaluation of community detection algorithms, which has two main novelties: 1) using complex closed benchmarks, which provide precise ways to assess whether the solutions generated by the algorithms are optimal; and, 2) A novel type of analysis, based on hierarchically clustering the solutions suggested by multiple community detection algorithms, which allows to easily visualize how different are those solutions. Surprise, a global parameter that evaluates the quality of a partition, confirms the power of these analyses. We show that none of the community detection algorithms tested provide consistently optimal results in all networks and that Surprise maximization, obtained by combining multiple algorithms, obtains quasi-optimal performances in these difficult benchmarks.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Aldecoa R,Marín Idoi
10.1038/srep02216subject
Has Abstractpub_date
2013-01-01 00:00:00pages
2216issn
2045-2322pii
srep02216journal_volume
3pub_type
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pub_type： 已发布勘误,撤回出版物
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pub_type： 杂志文章,meta分析
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