Community Detection Based on Surrogate Network

Lyu, C; Shi, Y; Sun, L

Community Detection Based on Surrogate Network

Lyu, C Shi, Y Sun, L

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Publisher:: Springer Nature
Publication Type:: Conference Proceeding
Citation:: Communications in Computer and Information Science, 2022, 1566 CCIS, pp. 45-53
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Lyu, C
dc.contributor.author	Shi, Y
dc.contributor.author	Sun, L https://orcid.org/0000-0002-9830-1527
dc.date.accessioned	2023-04-10T22:43:26Z
dc.date.available	2023-04-10T22:43:26Z
dc.date.issued	2022-01-01
dc.identifier.citation	Communications in Computer and Information Science, 2022, 1566 CCIS, pp. 45-53
dc.identifier.isbn	9789811912528
dc.identifier.issn	1865-0929
dc.identifier.issn	1865-0937
dc.identifier.uri	http://hdl.handle.net/10453/169404
dc.description.abstract	This paper presents a novel methodology to detect communities in complex networks based on evolutionary computation. In the proposed method, a surrogate network with a more detectable community structure than the original network is firstly constructed based on the eigenmatrix of the adjacent matrix. Then the community partition can be found by successively optimizing the modularity of the surrogate network and the original network with an evolutionary algorithm. The proposed method is tested on both synthetic and real-world networks and compared with some existing algorithms. Experimental results show that employing the constructed surrogate networks can effectively improve the community detection efficiency.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Communications in Computer and Information Science
dc.relation.ispartofseries	Communications in Computer and Information Science
dc.relation.isbasedon	10.1007/978-981-19-1253-5_4
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Community Detection Based on Surrogate Network
dc.type	Conference Proceeding
utslib.citation.volume	1566 CCIS
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-10T22:43:21Z
pubs.publication-status	Published
pubs.volume	1566 CCIS

Abstract:

This paper presents a novel methodology to detect communities in complex networks based on evolutionary computation. In the proposed method, a surrogate network with a more detectable community structure than the original network is firstly constructed based on the eigenmatrix of the adjacent matrix. Then the community partition can be found by successively optimizing the modularity of the surrogate network and the original network with an evolutionary algorithm. The proposed method is tested on both synthetic and real-world networks and compared with some existing algorithms. Experimental results show that employing the constructed surrogate networks can effectively improve the community detection efficiency.

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http://hdl.handle.net/10453/169404