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Conference Paper: A multiobjective hybrid evolutionary algorithm for clustering in social networks

TitleA multiobjective hybrid evolutionary algorithm for clustering in social networks
Authors
Issue Date2012
Citation
GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion, 2012, p. 1445-1446 How to Cite?
AbstractDetecting community structure is crucial for uncovering the links between structures and functions in complex networks. Most of contemporary community detection algorithms employ single optimization criteria (e.g., modularity), which may have fundamental disadvantages. This paper considers the community detection process as a Multi-Objective optimization Problem (MOP). To solve the community detection problem this study used modified harmony search algorithm (HAS), the original HAS often converges to local optima which is a disadvantage with this method. To avoid this shortcoming the HAS was combined with a Chaotic Local Search (CLS). In the proposed algorithm an external repository considered to save non-dominated solutions found during the search process and a fuzzy clustering technique was used to control the size of the repository. The experiments in synthetic and real networks show that the proposed multiobjective community detection algorithm is able to discover more accurate community structures. Copyright is held by the author/owner(s).
Persistent Identifierhttp://hdl.handle.net/10722/194435

 

DC FieldValueLanguage
dc.contributor.authorAmiri, B-
dc.contributor.authorHossain, L-
dc.contributor.authorCrawford, J-
dc.date.accessioned2014-01-30T03:32:35Z-
dc.date.available2014-01-30T03:32:35Z-
dc.date.issued2012-
dc.identifier.citationGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion, 2012, p. 1445-1446-
dc.identifier.urihttp://hdl.handle.net/10722/194435-
dc.description.abstractDetecting community structure is crucial for uncovering the links between structures and functions in complex networks. Most of contemporary community detection algorithms employ single optimization criteria (e.g., modularity), which may have fundamental disadvantages. This paper considers the community detection process as a Multi-Objective optimization Problem (MOP). To solve the community detection problem this study used modified harmony search algorithm (HAS), the original HAS often converges to local optima which is a disadvantage with this method. To avoid this shortcoming the HAS was combined with a Chaotic Local Search (CLS). In the proposed algorithm an external repository considered to save non-dominated solutions found during the search process and a fuzzy clustering technique was used to control the size of the repository. The experiments in synthetic and real networks show that the proposed multiobjective community detection algorithm is able to discover more accurate community structures. Copyright is held by the author/owner(s).-
dc.languageeng-
dc.relation.ispartofGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion-
dc.titleA multiobjective hybrid evolutionary algorithm for clustering in social networks-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1145/2330784.2330981-
dc.identifier.scopuseid_2-s2.0-84865018385-
dc.identifier.spage1445-
dc.identifier.epage1446-

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