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Role Analysis in Networks Using Mixtures of Exponential Random Graph Models

Authors: 

Michael Salter-Townsend, Brendan Murphy

Publication Type: 
Refereed Original Article
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.
Digital Object Identifer (DOI): 
10.1080/10618600.2014.923777
Publication Status: 
Published
Publication Date: 
13/06/2014
Journal: 
Journal of Computational and Graphical Statistics
Volume: 
24
Issue: 
2
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No