title: Role Analysis in Networks using Mixtures of Exponential Random Graph Models.
abstract: A novel and flexible framework for investigating the roles of actors within a network is introduced. Particular interest is in roles as defined by local network connectivity patterns, identified using the ego-networks extracted from the network. A mixture of Exponential-family Random Graph Models is developed for these ego-networks in order to cluster the nodes into roles. We refer to this model as the ego-ERGM. An Expectation-Maximization algorithm is developed to infer the unobserved cluster assignments and to estimate the mixture model parameters using a maximum pseudo-likelihood approximation. The flexibility and utility of the method are demonstrated on examples of simulated and real networks.
authors.affiliation: Dept. of Statistics, University of Oxford.
authors.forename: Michael
authors.initials: M
authors.lastname: Salter-Townshend
authors.affiliation: School of Mathematical Sciences, University College Dublin.
authors.forename: Thomas Brendan
authors.initials: TB
authors.lastname: Murphy
keywords: Expectation Maximisation algorithm; Exponential Random Graph Model; ego-network; finite mixture model