Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 461-466.doi: 10.11896/JsJkx.191100215

• Database & Big Data & Data Science • Previous Articles     Next Articles

Multi-obJective Evolutionary Algorithm Based on Community Detection Spectral Clustering

DONG Ming-gang, GONG Jia-ming and JING Chao   

  1. School of Information Science and Engineering,Guilin University of Technology,Guilin,Guangxi 541004,China
    Guangxi Key Laboratory of Embedded Technology and Intelligent System,Guilin,Guangxi 541004,China
  • Published:2020-07-07
  • About author:DONG Ming-gang, born in 1977, Ph.D.His main research interests include intelligent computing, multi-obJective optimization and machine learning.
    JING Chao, born in 1983, Ph.D.His main research interests include intelligent computing, optimization and deep reinforcement learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61563012,61802085),Natural Science Foundation of Guangxi,China (2014GXNSFAA118371,2015GXNSFBA139260), Guangxi Key Laboratory of Embedded Technology and Intelligent System Foundation(2018A-04) and Guangxi Graduate Program(YCSW201962).

Abstract: The multi-obJective optimization algorithm is competitive in the discovery of complex network communities.However,it is difficult to obtain the satisfied results while dealing with the problem of fuzzy community structure and large scale of network data.To overcome the shortcomings of existing multi-obJective methods,a multi-obJective complex network community discovery algorithm based on spectral clustering is proposed.The proposed algorithm uses spectral clustering to perform initial po-pulation partitioning on the encoded complex network,and exploits its subgraph clustering characteristics to obtain a better initial population.A data reduction method based on grid reduction is applied to reduce the population in the process of evolution,which effectively reduces the complexity of the algorithm.The experimental results on the simulation network and the real network show that the proposed algorithm outperforms than that of the other three representative multi-target based community discovery algorithms in terms of community discovery performance and computational complexity.

Key words: Multi-obJective evolutionary, Community discovery, Complex network, Large-scale network

CLC Number: 

  • TP391
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