Community Detection in Networks with Node Features

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. Community detection is a fundamental problem in network analysis, extensively studied in a number of domains - see (1) and (2) for some examples of applications. A number of approaches to community detection are based on probabilistic models for networks with communities, such as the stochastic block model (3), the degree-corrected stochastic block model (4), and the latent factor model (5). Other approaches work by optimizing a criterion measuring the strength of community structure in some sense, often through spectral approximations. Examples include normalized cuts (6), modularity (7; 8), and many variants of spectral clustering, e.g., (9).