Salter-Townshend, MichaelMichaelSalter-TownshendMurphy, Thomas BrendanThomas BrendanMurphy2017-03-282017-03-282015 Taylo2015Journal of Computational and Graphical Statisticshttp://hdl.handle.net/10197/8406This article introduces a novel and flexible framework for investigating the roles of actors within a network. 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 (ERGM) is developed for these ego-networks 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. We demonstrate the flexibility and utility of the method using examples of simulated and real networks.enThis is an electronic version of an article published in Journal of Computational and Graphical Statistics 24(2): 520-538 (2015). Journal of Computational and Graphical Statistics is available online at: www.tandfonline.com/doi/abs/10.1080/10618600.2014.923777.Machine learningStatisticsEgo-networkExpectation maximisation algorithmFinite mixture modelExponential random graph modelJournal Article24252053810.1080/10618600.2014.9237772016-11-15https://creativecommons.org/licenses/by-nc-nd/3.0/ie/