Identification of hybrid node and link communities in complex networks

Dongxiao He, Di Jin, Zheng Chen, Weixiong Zhang

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


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.

Original languageEnglish
Article number8638
Pages (from-to)1-14
Number of pages14
JournalScientific reports
StatePublished - 2015


Dive into the research topics of 'Identification of hybrid node and link communities in complex networks'. Together they form a unique fingerprint.

Cite this