  Identification of communities in complex networks has become an effective
means to analysis of complex systems. It has broad 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 structure analysis. These schemes, however, have inherent drawbacks
and are often inadequate to properly capture complex organizational structures
in real networks. We introduce a new scheme and effective approach for
identifying complex network structures using a mixture 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
semantic association network of commonly used 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.
