A multiobjective discrete bat algorithm for community detection in dynamic networks

Some evolutionary based clustering approaches for community detection in dynamic networks need an input parameter to control the preference degree of snapshot and temporal cost. To break the limitation of parameter selection and improve the quality of detecting communities in dynamic network further...

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Vydáno v:Applied intelligence (Dordrecht, Netherlands) Ročník 48; číslo 9; s. 3081 - 3093
Hlavní autoři: Zhou, Xu, Zhao, Xiaohui, Liu, Yanheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.09.2018
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Clustering
Communities
Computer Science
Evolutionary algorithms
Machines
Manufacturing
Mechanical Engineering
Multiple objective analysis
Networks
Processes
Turbulence
Community detection
Multiobjective bat algorithm
Swarm intelligence
ISSN:0924-669X, 1573-7497
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Shrnutí:Some evolutionary based clustering approaches for community detection in dynamic networks need an input parameter to control the preference degree of snapshot and temporal cost. To break the limitation of parameter selection and improve the quality of detecting communities in dynamic network further, a multiobjective discrete bat algorithm (MDBA) is proposed to detect community structure in dynamic networks in this paper. In the proposed algorithm, the bat location updating strategy is designed in discrete form. In addition, turbulence operation and mutation strategy are presented to guarantee the diversity of the population. The non-dominated sorting and crowding distance mechanism are used to keep good solutions during the generation. The experimental results both on synthetic and real networks show that MDBA algorithm is competitive and will get higher accuracy and lower error rate than the compared algorithms.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-017-1135-5