Community Detection in Social and Biological Networks Using Differential Evolution

Jia, Guanbo; Cai, Zixing; Musolesi, Mirco; Wang, Yong; Tennant, Daniel A.; Weber, Ralf; Heath, John K.; He, Shan

doi:10.1007/978-3-642-34413-8_6

Cited by 57 publications

(33 citation statements)

References 28 publications

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“…2, the horizontal axis represents the number of inter community edges Zout and the vertical axis represents the fraction of vertices classified correctly. It is clear that our VSDE performs significantly better than DECD [7] in the range Z out > 7 while it performs almost same when Z out <=7. The performance of VSDE was further tested on two realworld social networks, Zachary's karate club network [12] and American college football network [13].…”

Section: Vsde Has Been Implemented In Matlab and On Windowsmentioning

confidence: 67%

“…We have presented VSDE as an improvisation of existing differential evolution technique [7] to identify community in complex networks. This algorithm requires no prior information about the networks which makes VSDE suitable for real world applications.…”

Section: Resultsmentioning

confidence: 99%

“…After this step, the mutant vector is checked if any of its components violate the boundary constraints. If they do, then they are corrected as in DECD [7].…”

Section: Differential Evolutionmentioning

confidence: 99%

“…It comes under the class of evolutionary algorithms which also includes Genetic algorithms and others. Differential evolution has also been employed earlier for community detection in complex networks [7]. In this paper, we present an improvised DE based approach with biased initialization based on the concept of vertex similarity [5].…”

Section: Differential Evolutionmentioning

confidence: 99%

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Improvisation of differential evolution for community detection

Kumar

Gupta

Singh

et al. 2014

2014 IEEE International Conference on Computational Intelligence and Computing Research

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Most of the real world networks we encounter today are complex networks and one of the important characteristics of these networks is the community structure. Identifying communities in a complex network is classified as computably hard and thus many metaheuristic approaches have been proposed in the past. In this paper we propose an improved differential evolution based algorithm which exploits the structural similarity of the network to generate a better initial population leading to a more accurate identification of communities. We have tested our algorithm on various well-known real world and artificial networks.

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Section: Vsde Has Been Implemented In Matlab and On Windowsmentioning

confidence: 67%

Section: Resultsmentioning

confidence: 99%

“…After this step, the mutant vector is checked if any of its components violate the boundary constraints. If they do, then they are corrected as in DECD [7].…”

Section: Differential Evolutionmentioning

confidence: 99%

Section: Differential Evolutionmentioning

confidence: 99%

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Improvisation of differential evolution for community detection

Kumar

Gupta

Singh

et al. 2014

2014 IEEE International Conference on Computational Intelligence and Computing Research

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“…As effective tools to solve combinatorial problems, metaheuristic algorithms have been widely applied to search satisfied solutions [7, 8]. The original paper [5] proposed to use simulated annealing (SA), a generic probabilistic metaheuristic to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Active module identification in intracellular networks using a memetic algorithm with a new binary decoding scheme

Pan

et al. 2017

BMC Genomics

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BackgroundActive modules are connected regions in biological network which show significant changes in expression over particular conditions. The identification of such modules is important since it may reveal the regulatory and signaling mechanisms that associate with a given cellular response.ResultsIn this paper, we propose a novel active module identification algorithm based on a memetic algorithm. We propose a novel encoding/decoding scheme to ensure the connectedness of the identified active modules. Based on the scheme, we also design and incorporate a local search operator into the memetic algorithm to improve its performance.ConclusionThe effectiveness of proposed algorithm is validated on both small and large protein interaction networks.

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