Abstract:
:Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.
journal_name
Sci Repjournal_title
Scientific reportsauthors
He D,Jin D,Chen Z,Zhang Wdoi
10.1038/srep08638subject
Has Abstractpub_date
2015-03-02 00:00:00pages
8638issn
2045-2322pii
srep08638journal_volume
5pub_type
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