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author:

Guo, Kun (Guo, Kun.) [1] | Chen, Zhanhong (Chen, Zhanhong.) [2] | Lin, Xu (Lin, Xu.) [3] | Wu, Ling (Wu, Ling.) [4] | Zhan, Zhi-Hui (Zhan, Zhi-Hui.) [5] | Chen, Yuzhong (Chen, Yuzhong.) [6] | Guo, Wenzhong (Guo, Wenzhong.) [7]

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EI

Abstract:

Community detection is an important research direction in complex network analysis that can help us discover valuable network structures. The community detection algorithms based on multiobjective particle swarm optimization encode community membership of nodes in particles and employ evolutionary strategies to search for the optimal community division. Existing algorithms face two challenges: (1) they are inapplicable to large networks because the evolution process is time-consuming; (2) they are easy to fall into local optima. In this paper, we propose a novel algorithm that combines a label-propagation-based multiobjective particle swarm optimization algorithm with a graph attention variational autoencoder to realize community detection. On the one hand, the label propagation strategy is involved in the update of a swarm's particles to speed up its evolution. The optimal solutions found by the particle swarm optimization algorithm are embedded into the objective of the autoencoder to improve the embedding vectors' quality. On the other hand, the embedding vectors are used to improve the solutions of the particle swarm optimization algorithm to avoid its early convergence. The experiments on artificial and real-world networks demonstrate the feasibility and effectiveness of our algorithm compared with some state-of-the-art algorithms. © 2015 IEEE.

Keyword:

Big data Complex networks Embeddings Multiobjective optimization Particle swarm optimization (PSO) Population dynamics Signal detection

Community:

  • [ 1 ] [Guo, Kun]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Guo, Kun]College of Computer and Data Science, Fuzhou University, Fuzhou; 350108, China
  • [ 3 ] [Guo, Kun]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou; 350108, China
  • [ 4 ] [Chen, Zhanhong]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou; 350108, China
  • [ 5 ] [Chen, Zhanhong]College of Computer and Data Science, Fuzhou University, Fuzhou; 350108, China
  • [ 6 ] [Chen, Zhanhong]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou; 350108, China
  • [ 7 ] [Lin, Xu]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou; 350108, China
  • [ 8 ] [Lin, Xu]College of Computer and Data Science, Fuzhou University, Fuzhou; 350108, China
  • [ 9 ] [Lin, Xu]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou; 350108, China
  • [ 10 ] [Wu, Ling]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou; 350108, China
  • [ 11 ] [Wu, Ling]College of Computer and Data Science, Fuzhou University, Fuzhou; 350108, China
  • [ 12 ] [Wu, Ling]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou; 350108, China
  • [ 13 ] [Zhan, Zhi-Hui]School of Computer Science and Engineering, South China University of Technology, Guangzhou; 510006, China
  • [ 14 ] [Chen, Yuzhong]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou; 350108, China
  • [ 15 ] [Chen, Yuzhong]College of Computer and Data Science, Fuzhou University, Fuzhou; 350108, China
  • [ 16 ] [Chen, Yuzhong]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou; 350108, China
  • [ 17 ] [Guo, Wenzhong]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou; 350108, China
  • [ 18 ] [Guo, Wenzhong]College of Computer and Data Science, Fuzhou University, Fuzhou; 350108, China
  • [ 19 ] [Guo, Wenzhong]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou; 350108, China

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Source :

IEEE Transactions on Big Data

Year: 2023

Issue: 2

Volume: 9

Page: 569-583

7 . 5

JCR@2023

7 . 5 0 0

JCR@2023

ESI HC Threshold:32

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 3

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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