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Identification of communities in complex networks has become an effectivemeans to analysis of complex systems. It has broad applications in diverseareas such as social science, engineering, biology and medicine. Findingcommunities of nodes and finding communities of links are two popular schemesfor network structure analysis. These schemes, however, have inherent drawbacksand are often inadequate to properly capture complex organizational structuresin real networks. We introduce a new scheme and effective approach foridentifying complex network structures using a mixture of node and linkcommunities, called hybrid node-link communities. A central piece of ourapproach is a probabilistic model that accommodates node, link and hybridnode-link communities. Our extensive experiments on various real-worldnetworks, including a large protein-protein interaction network and a largesemantic association network of commonly used words, illustrated that thescheme for hybrid communities is superior in revealing network characteristics.Moreover, the new approach outperformed the existing methods for finding nodeor link communities separately.

Identification of hybrid node and link communities in complex networks