A multi-objective adaptive evolutionary algorithm to extract communities in networks

Li, Q; Cao, Z; Ding, W; Li, Q

A multi-objective adaptive evolutionary algorithm to extract communities in networks

Li, Q Cao, Z

Ding, W Li, Q

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Swarm and Evolutionary Computation, 2020, 52
Issue Date:: 2020-02-01

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Field	Value	Language
dc.contributor.author	Li, Q
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.contributor.author	Ding, W
dc.contributor.author	Li, Q
dc.date.accessioned	2020-10-17T19:53:26Z
dc.date.available	2020-10-17T19:53:26Z
dc.date.issued	2020-02-01
dc.identifier.citation	Swarm and Evolutionary Computation, 2020, 52
dc.identifier.issn	2210-6502
dc.identifier.issn	2210-6510
dc.identifier.uri	http://hdl.handle.net/10453/143338
dc.description.abstract	© 2019 Community structure is one of the most important attributes of complex networks, which reveals the hidden rules and behavior characteristics of complex networks. Existing works need to pre-set weight parameters to control the different emphasis on the objective function, and cannot automatically identify the number of communities. In the process of optimization, there will be some challenges, such as premature and inefficiency. This paper presents a multi-objective adaptive fast evolutionary algorithm (F-SGCD) for community detection in complex networks. Firstly, it transforms the problem of community detection into a multi-objective optimization problem and constructs two objective functions of community score and community fitness. Secondly, an external elite gene pool is introduced to store non-inferior solutions with high fitness. At the same time, an adaptive genetic operator is executed to return a set of non-dominant solutions compromised between the two objective functions. Finally, a Pareto optimal solution with the highest modularity is selected and decoded to generate a set of independent subnetworks. Experiments show that the multi-objective adaptive fast evolutionary algorithm greatly improves the accuracy of community detection in complex networks, and can discover the hierarchical structure of complex networks better.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Swarm and Evolutionary Computation
dc.relation.isbasedon	10.1016/j.swevo.2019.100629
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0802 Computation Theory and Mathematics
dc.title	A multi-objective adaptive evolutionary algorithm to extract communities in networks
dc.type	Journal Article
utslib.citation.volume	52
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0802 Computation Theory and Mathematics
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
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status	closed_access	*
dc.date.updated	2020-10-17T19:53:22Z
pubs.publication-status	Published
pubs.volume	52

Abstract:

© 2019 Community structure is one of the most important attributes of complex networks, which reveals the hidden rules and behavior characteristics of complex networks. Existing works need to pre-set weight parameters to control the different emphasis on the objective function, and cannot automatically identify the number of communities. In the process of optimization, there will be some challenges, such as premature and inefficiency. This paper presents a multi-objective adaptive fast evolutionary algorithm (F-SGCD) for community detection in complex networks. Firstly, it transforms the problem of community detection into a multi-objective optimization problem and constructs two objective functions of community score and community fitness. Secondly, an external elite gene pool is introduced to store non-inferior solutions with high fitness. At the same time, an adaptive genetic operator is executed to return a set of non-dominant solutions compromised between the two objective functions. Finally, a Pareto optimal solution with the highest modularity is selected and decoded to generate a set of independent subnetworks. Experiments show that the multi-objective adaptive fast evolutionary algorithm greatly improves the accuracy of community detection in complex networks, and can discover the hierarchical structure of complex networks better.

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http://hdl.handle.net/10453/143338