Identification of hybrid node and link communities in complex networks.

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 Rep

journal_title

Scientific reports

authors

He D,Jin D,Chen Z,Zhang W

doi

10.1038/srep08638

subject

Has Abstract

pub_date

2015-03-02 00:00:00

pages

8638

issn

2045-2322

pii

srep08638

journal_volume

5

pub_type

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