Role Analysis in Networks Using Mixtures of Exponential Random Graph Models

Files in This Item:
File Description SizeFormat 
insight_publication.pdf773.49 kBAdobe PDFDownload
Title: Role Analysis in Networks Using Mixtures of Exponential Random Graph Models
Authors: Salter-Townshend, Michael
Murphy, Thomas Brendan
Permanent link:
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

Show full item record

Citations 50

Last Week
Last month
checked on Aug 18, 2018

Download(s) 50

checked on May 25, 2018

Google ScholarTM



This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.