Efficient discovery of overlapping communities in massive networks.

ABSTRACT: Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 p

SUBMITTER: Gopalan PK 

PROVIDER: S-EPMC3767539 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature