Community detection using cooperative co-evolutionary differential evolution

Qiang Huang; Thomas White; Guanbo Jia; Mirco Musolesi; Nil Turan; Ke Tang; Shan He; John K. Heath; Xin Yao

doi:10.1007/978-3-642-32964-7_24

Community detection using cooperative co-evolutionary differential evolution

Qiang Huang, Thomas White, Guanbo Jia, Mirco Musolesi, Nil Turan, Ke Tang, Shan He^*, John K. Heath, Xin Yao

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

20 Citations (Scopus)

Abstract

In many scientific fields, from biology to sociology, community detection in complex networks has become increasingly important. This paper, for the first time, introduces Cooperative Co-evolution framework for detecting communities in complex networks. A Bias Grouping scheme is proposed to dynamically decompose a complex network into smaller subnetworks to handle large-scale networks. We adopt Differential Evolution (DE) to optimize network modularity to search for an optimal partition of a network. We also design a novel mutation operator specifically for community detection. The resulting algorithm, Cooperative Co-evolutionary DE based Community Detection (CCDECD) is evaluated on 5 small to large scale real-world social and biological networks. Experimental results show that CCDECD has very competitive performance compared with other state-of-the-art community detection algorithms.

Original language	English
Title of host publication	Parallel Problem Solving from Nature, PPSN XII - 12th International Conference, Proceedings
Pages	235-244
Number of pages	10
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-32964-7_24
Publication status	Published - 2012
Event	12th International Conference on Parallel Problem Solving from Nature, PPSN 2012 - Taormina, Italy Duration: 1 Sept 2012 → 5 Sept 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7492 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Parallel Problem Solving from Nature, PPSN 2012
Country/Territory	Italy
City	Taormina
Period	1/09/12 → 5/09/12

Bibliographical note

Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-32964-7_24

Cite this

Huang, Q., White, T., Jia, G., Musolesi, M., Turan, N., Tang, K., He, S., Heath, J. K., & Yao, X. (2012). Community detection using cooperative co-evolutionary differential evolution. In Parallel Problem Solving from Nature, PPSN XII - 12th International Conference, Proceedings (PART 2 ed., pp. 235-244). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7492 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-32964-7_24

Huang, Qiang ; White, Thomas ; Jia, Guanbo et al. / Community detection using cooperative co-evolutionary differential evolution. Parallel Problem Solving from Nature, PPSN XII - 12th International Conference, Proceedings. PART 2. ed. 2012. pp. 235-244 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{032cb7ef49f54605a10af26586b04174,

title = "Community detection using cooperative co-evolutionary differential evolution",

abstract = "In many scientific fields, from biology to sociology, community detection in complex networks has become increasingly important. This paper, for the first time, introduces Cooperative Co-evolution framework for detecting communities in complex networks. A Bias Grouping scheme is proposed to dynamically decompose a complex network into smaller subnetworks to handle large-scale networks. We adopt Differential Evolution (DE) to optimize network modularity to search for an optimal partition of a network. We also design a novel mutation operator specifically for community detection. The resulting algorithm, Cooperative Co-evolutionary DE based Community Detection (CCDECD) is evaluated on 5 small to large scale real-world social and biological networks. Experimental results show that CCDECD has very competitive performance compared with other state-of-the-art community detection algorithms.",

author = "Qiang Huang and Thomas White and Guanbo Jia and Mirco Musolesi and Nil Turan and Ke Tang and Shan He and Heath, {John K.} and Xin Yao",

note = "Copyright: Copyright 2012 Elsevier B.V., All rights reserved.; 12th International Conference on Parallel Problem Solving from Nature, PPSN 2012 ; Conference date: 01-09-2012 Through 05-09-2012",

year = "2012",

doi = "10.1007/978-3-642-32964-7_24",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Huang, Q, White, T, Jia, G, Musolesi, M, Turan, N, Tang, K, He, S, Heath, JK & Yao, X 2012, Community detection using cooperative co-evolutionary differential evolution. in Parallel Problem Solving from Nature, PPSN XII - 12th International Conference, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7492 LNCS, pp. 235-244, 12th International Conference on Parallel Problem Solving from Nature, PPSN 2012, Taormina, Italy, 1/09/12. https://doi.org/10.1007/978-3-642-32964-7_24

Community detection using cooperative co-evolutionary differential evolution. / Huang, Qiang; White, Thomas; Jia, Guanbo et al.
Parallel Problem Solving from Nature, PPSN XII - 12th International Conference, Proceedings. PART 2. ed. 2012. p. 235-244 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7492 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Community detection using cooperative co-evolutionary differential evolution

AU - Huang, Qiang

AU - White, Thomas

AU - Jia, Guanbo

AU - Musolesi, Mirco

AU - Turan, Nil

AU - Tang, Ke

AU - He, Shan

AU - Heath, John K.

AU - Yao, Xin

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AB - In many scientific fields, from biology to sociology, community detection in complex networks has become increasingly important. This paper, for the first time, introduces Cooperative Co-evolution framework for detecting communities in complex networks. A Bias Grouping scheme is proposed to dynamically decompose a complex network into smaller subnetworks to handle large-scale networks. We adopt Differential Evolution (DE) to optimize network modularity to search for an optimal partition of a network. We also design a novel mutation operator specifically for community detection. The resulting algorithm, Cooperative Co-evolutionary DE based Community Detection (CCDECD) is evaluated on 5 small to large scale real-world social and biological networks. Experimental results show that CCDECD has very competitive performance compared with other state-of-the-art community detection algorithms.

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Huang Q, White T, Jia G, Musolesi M, Turan N, Tang K et al. Community detection using cooperative co-evolutionary differential evolution. In Parallel Problem Solving from Nature, PPSN XII - 12th International Conference, Proceedings. PART 2 ed. 2012. p. 235-244. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-32964-7_24

Community detection using cooperative co-evolutionary differential evolution

Abstract

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ASJC Scopus subject areas

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Cite this