Role Analysis in Networks Using Mixtures of Exponential Random Graph Models
|Title:||Role Analysis in Networks Using Mixtures of Exponential Random Graph Models||Authors:||Salter-Townshend, Michael
Murphy, Thomas Brendan
|Permanent link:||http://hdl.handle.net/10197/8406||Date:||2015||Abstract:||This 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.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||Taylor and Francis||Copyright (published version):||2015 Taylor and Francis||Keywords:||Machine learning;Statistics;Ego-network;Expectation maximisation algorithm;Finite mixture model;Exponential random graph model||DOI:||10.1080/10618600.2014.923777||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mathematics and Statistics Research Collection|
Insight Research Collection
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