Community Detection in Networks with Node Features

Abstract

Many methods have been proposed for community detection in networks, but most of them do not take into account additional information on the nodes that is often available in practice. In this paper, we propose a new joint community detection criterion that uses both the network edge information and the node features to detect community structures. One advantage our method has over existing joint detection approaches is the flexibility of learning the impact of different features which may differ across communities. Another advantage is the flexibility of choosing the amount of influence the feature information has on communities. The method is asymptotically consistent under the block model with additional assumptions on the feature distributions, and performs well on simulated and real networks.

16 pages, 5 pages

References in corpus (4)

• Modularity and community structure in networks
• Stochastic blockmodels and community structure in networks
• Modeling homophily and stochastic equivalence in symmetric relational data
• Partitioning Networks with Node Attributes by Compressing Information Flow

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