Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 110, Issue 36, Pages 14534-14539Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1221839110
Keywords
network analysis; Bayesian statistics; massive data
Categories
Funding
- Office of Naval Research [N00014-11-1-0651]
- National Science Foundation [0745520]
- BIGDATA [IIS-1247664]
- Alfred P. Sloan Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1247664] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0745520] Funding Source: National Science Foundation
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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 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks.
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