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General Information
    • ISSN: 1793-8244 (Print)
    • Abbreviated Title:  J. Adv. Comput. Netw.
    • Frequency: Semiyearly
    • DOI: 10.18178/JACN
    • Editor-in-Chief: Professor Haklin Kimm
    • Executive Editor: Ms. Cherry Chan
    • Abstracting/ Indexing: EBSCO, ProQuest, and Google Scholar.
    • E-mail: jacn@ejournal.net
    • APC: 500USD
Professor Haklin Kimm
East Stroudsburg University, USA
I'm happy to take on the position of editor in chief of JACN. We encourage authors to submit papers on all aspects of computer networks.

JACN 2013 Vol.1(2): 88-93 ISSN: 1793-8244
DOI: 10.7763/JACN.2013.V1.19

Network Community Structure Clustering Algorithm Based on the Genetic Theory

Nan Lu, Yuanyuan Jin, and Lei Qin

Abstract—This paper proposes the idea of applying a clustering ensemble based genetic algorithm in the area of complex social network mining. The algorithm introduces clustering ensemble into the crossover operator and employs the clustering information of the parents to generate new individuals, which avoids the problems that caused by simply exchanging string between crossover operators without consider the contents. In population generation, Markov random walk strategy is employed to maintain the diversity of the individuals as well as the clustering accuracy. The algorithm also uses a local searching mechanism in crossover operators to reduce the searching space and improve the speed of convergence. Comparing with existing mining algorithms in social network, the proposed algorithm is more effective proved by experiments in both simulation and real world social networks.

Index Terms—Community structure, complex network, genetic algorithm, clustering ensemble, data mining.

The authors are with the College of computer and software, Shenzhen University, Shenzhen 518060, Guangdong, China (e-mail: lunan@szu.edu.cn, 690485801@qq.com, qinlei626@gmail.com).


Cite:Nan Lu, Yuanyuan Jin, and Lei Qin, "Network Community Structure Clustering Algorithm Based on the Genetic Theory," Journal of Advances in Computer Networks vol. 1, no. 2, pp. 88-93, 2013.

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