Tracking the evolution of communities in dynamic social networks

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Title: Tracking the evolution of communities in dynamic social networks
Authors: Greene, Derek
Doyle, Dónal
Cunningham, Pádraig
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Date: 11-Aug-2010
Abstract: Real-world social networks from a variety of domains can naturally be modelled as dynamic graphs. However, approaches to detecting communities have largely focused on identifying communities in static graphs. Recently, researchers have begun to consider the problem of tracking the evolution of groups of users in dynamic scenarios. Here we describe a model for tracking the progress of communities over time in a dynamic network, where each community is characterised by a series of significant evolutionary events. This model is used to motivate a community-matching strategy for efficiently identifying and tracking dynamic communities. Evaluations on synthetic graphs containing embedded events demonstrate that this strategy can successfully track communities over time in volatile networks. In addition, we describe experiments exploring the dynamic communities detected in a real mobile operator network containing millions of users.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2010 IEEE
2010 by The Institute of Electrical and Electronics Engineers, Inc.
Keywords: Social network analysisMachine learningCommunity finding
Subject LCSH: Online social networks--Computer simulation
Social groups--Computer simulation
Machine learning
DOI: 10.1109/ASONAM.2010.17
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Language: en
Status of Item: Peer reviewed
Is part of: N. Memon and R. Alhajj (ed.s). 2010 2010 International Conference on Advances in Social Network Analysis and Mining : ASONAM 2010 : proceedings
Conference Details: 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2010), 9-11 August 2010, Odense, Denmark
ISBN: 978-1-4244-7787-6
Appears in Collections:Computer Science Research Collection

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