Role Analysis in Networks Using Mixtures of Exponential Random Graph Models

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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 learningStatisticsEgo-networkExpectation maximisation algorithmFinite mixture modelExponential 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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