Advanced Review Bayesian Vertex Nomination Using Content and Context

Using attributed graphs to model network data has become an attractive approach for various graph inference tasks. Consider a network containing a small subset of interesting entities whose identities are not fully known and that discovering them will be of some significance. Vertex nomination, a subclass of recommender systems relying on the exploitation of attributed graphs, is a task which seeks to identify the unknown entities that are similarly interesting or exhibit analogous latent attributes. This task is a specific type of community detection and is increasingly becoming a subject of current research in many disciplines. Recent studies have shown that information relevant to this task is contained in both the structure of the network and its attributes, and that jointly exploiting them can provide superior vertex nomination performance than either one used alone. We adopt this new approach to formulate a Bayesian model for the vertex nomination problem. Specifically, the goal here is to construct a ‘nomination list’ where entities that are truly interesting are concentrated at the top of the list. Inference with themodel is conducted using a Metropolis-within-Gibbs algorithm. Performance of the model is illustrated by a Monte Carlo simulation study and on the well-known Enron email dataset. © 2015 Wiley Periodicals, Inc.