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Abstract Details

Activity Number: 391
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract - #305883
Title: Role Analysis in Networks Using a Mixture of Exponential Random Graph Models
Author(s): Thomas Brendan Murphy*+ and Michael Salter-Townshend
Companies: University College Dublin and University College Dublin
Address: School of Mathematical Sciences, Dublin 4, , Ireland
Keywords: Network ; Social Network ; ERGM ; Finite Mixture Model ; Clustering

A novel and flexible framework for the study of roles to actors within a social network is developed. Particular interest is in roles as defined by local network connectivity patterns and thus the ego-centric networks are extracted for each node in the network. A mixture of Exponential-family Random Graph Models is developed for these ego-networks in order to cluster the nodes to roles.

An Expectation-Maximization algorithm is used to infer the unobserved cluster assignments and to estimate the mixture model parameters using a maximum pseudolikelihood approximation. The flexibility and utility of the method are demonstrated on several examples.

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