A genetic algorithm with local search strategy for improved detection of community structure |
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| Authors: | Shuzhuo Li Yinghui Chen Haifeng Du Marcus W. Feldman |
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| Affiliation: | 1. Institute for Population and Development Studies;2. School of Management;3. Center for Administration and Complexity Science, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China;4. Morrison Institute for Population and Resource Studies, Stanford University, Stanford, California 94305 |
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| Abstract: | On the basis of modularity optimization, a genetic algorithm is proposed to detect community structure in networks by defining a local search operator. The local search operator emphasizes two features: one is that the connected nodes in a network should be located in the same community, while the other is “local selection” inspired by the mechanisms of efficient message delivery underlying the small‐world phenomenon. The results of community detection for some classic networks, such as Ucinet and Pajek networks, indicate that our algorithm achieves better community structure than other methodologies based on modularity optimization, such as the algorithms based on betweenness analysis, simulated annealing, or Tasgin and Bingol's genetic algorithm. © 2009 Wiley Periodicals, Inc. Complexity, 2010 |
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| Keywords: | network structure modularity genetic algorithm small‐world phenomena |
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