Community Detection in Networks with Node Features

• Activity Number: 366
• Type: Invited
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: General Methodology
• Author(s): Yuan Zhang and Elizaveta Levina*+ and Ji Zhu
• Companies: University of Michigan and University of Michigan and University of Michigan
• Keywords: networks ; node features ; modularity ; community detection ; stochastic block model

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. We propose a new joint community detection criterion that uses both the network and the features to detect community structure. 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.