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