Evolutionary Community Detection in Dynamic Social Networks

Liu, F; Wu, J; Zhou, C; Yang, J

Evolutionary Community Detection in Dynamic Social Networks

Liu, F Wu, J Zhou, C Yang, J

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Publisher:: OEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2019, 2019-July
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Liu, F
dc.contributor.author	Wu, J
dc.contributor.author	Zhou, C
dc.contributor.author	Yang, J
dc.date	2019-07-14
dc.date.accessioned	2020-06-13T07:13:17Z
dc.date.available	2020-06-13T07:13:17Z
dc.date.issued	2019-07-01
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2019, 2019-July
dc.identifier.isbn	9781728119854
dc.identifier.issn	2161-4393
dc.identifier.issn	2161-4407
dc.identifier.uri	http://hdl.handle.net/10453/141408
dc.description.abstract	© 2019 IEEE. Evolutionary clustering is a way of detecting the evolving patterns of communities in dynamic social networks. In principle, the detection process seeks to simultaneously maximize clustering accuracy at the current time step and minimize the clustering drift between two successive time steps. Several evolutionary clustering methods have been developed in an attempt to find the best trade-off between clustering accuracy and temporal smoothness, but the classic genetic operators in these methods do not make the best of the inter- and intra-connected relationships between nodes, which limits their effectiveness. To overcome this problem, we propose a novel migration operator to work in tandem with classic genetic operators to improve the discovery of evolving community structures. The operator is implemented within an existing genetic algorithm which relies on a genome representation under a decomposition framework that formulates evolutionary community detection as a multiobjective optimization problem. Moreover, we present a new method of calculating modularity directly from a genome matrix as the objective for measuring the snapshot quality, which results in a wider search space for finding the optimal solution. Experimental results over several synthetic networks and one real-world dynamic social network suggest that our method is superior to two other state-of-the-art methods in terms of both accuracy and smoothness in discovering evolving community structures in dynamic social networks.
dc.language	en
dc.publisher	OEEE
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks
dc.relation.ispartof	International Joint Conference on Neural Networks
dc.relation.isbasedon	10.1109/IJCNN.2019.8852006
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Evolutionary Community Detection in Dynamic Social Networks
dc.type	Conference Proceeding
utslib.citation.volume	2019-July
utslib.location.activity	Budapest, Hungary14 Jun 2019
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-13T07:13:14Z
pubs.finish-date	2019-07-19
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-07-14
pubs.volume	2019-July
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. Evolutionary clustering is a way of detecting the evolving patterns of communities in dynamic social networks. In principle, the detection process seeks to simultaneously maximize clustering accuracy at the current time step and minimize the clustering drift between two successive time steps. Several evolutionary clustering methods have been developed in an attempt to find the best trade-off between clustering accuracy and temporal smoothness, but the classic genetic operators in these methods do not make the best of the inter- and intra-connected relationships between nodes, which limits their effectiveness. To overcome this problem, we propose a novel migration operator to work in tandem with classic genetic operators to improve the discovery of evolving community structures. The operator is implemented within an existing genetic algorithm which relies on a genome representation under a decomposition framework that formulates evolutionary community detection as a multiobjective optimization problem. Moreover, we present a new method of calculating modularity directly from a genome matrix as the objective for measuring the snapshot quality, which results in a wider search space for finding the optimal solution. Experimental results over several synthetic networks and one real-world dynamic social network suggest that our method is superior to two other state-of-the-art methods in terms of both accuracy and smoothness in discovering evolving community structures in dynamic social networks.

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http://hdl.handle.net/10453/141408