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Conference Paper: A hybrid evolutionary algorithm based on HSA and CLS for multi-objective community detection in complex networks

TitleA hybrid evolutionary algorithm based on HSA and CLS for multi-objective community detection in complex networks
Authors
Issue Date2012
Citation
Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, 2012, p. 243-247 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 multi-objective community detection algorithm is able to discover more accurate community structures. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/194376
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorAmiri, B-
dc.contributor.authorHossain, L-
dc.contributor.authorCrawford, J-
dc.date.accessioned2014-01-30T03:32:31Z-
dc.date.available2014-01-30T03:32:31Z-
dc.date.issued2012-
dc.identifier.citationProceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, 2012, p. 243-247-
dc.identifier.urihttp://hdl.handle.net/10722/194376-
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 multi-objective community detection algorithm is able to discover more accurate community structures. © 2012 IEEE.-
dc.languageeng-
dc.relation.ispartofProceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012-
dc.titleA hybrid evolutionary algorithm based on HSA and CLS for multi-objective community detection in complex networks-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ASONAM.2012.49-
dc.identifier.scopuseid_2-s2.0-84874228886-
dc.identifier.spage243-
dc.identifier.epage247-
dc.identifier.isiWOS:000320443500035-

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