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Network community partition based on intelligent clustering algorithm
Z.M. Cai 1

1 Information Engineering College, Henan University of Animal Husbandry and Economy,
Zhengzhou, Henan 450044, China

 PDF, 712 kB

DOI: 10.18287/2412-6179-CO-724

Pages: 985-989.

Full text of article: English language.

Abstract:
The division of network community is an important part of network research. Based on the clustering algorithm, this study analyzed the partition method of network community. Firstly, the classic Louvain clustering algorithm was introduced, and then it was improved based on the node similarity to get better partition results. Finally, experiments were carried out on the random network and the real network. The results showed that the improved clustering algorithm was faster than GN and KL algorithms, the community had larger modularity, and the purity was closer to 1. The experimental results show the effectiveness of the proposed method and make some contributions to the reliable community division.

Keywords:
clustering algorithm, network community, node similarity, community division.

Citation:
Cai ZM. Network community partition based on intelligent clustering algorithm. Computer Optics 2020; 44(6): 985-989. DOI: 10.18287/2412-6179-CO-724.

References:
  1. Mostovoi JA. The two-phase operation in large-scale network. Computer Optics 2013; 37(1): 120-130.
  2. Pizzuti C. Evolutionary computation for community detection in networks: A review. IEEE Trans Evol Comput 2018; 22(3): 464-483.
  3. Reihanian A, Minaei B, Alizadeh H. Topic-oriented community detection of rating-based social networks. J King Saud Univ Sci – Computer and Information Sciences 2016; 28(3): 303-310.
  4. Rossetti G, Pappalardo L, Pedreschi D, Giannotti F. Tiles: an online algorithm for community discovery in dynamic social networks. Mach Learn 2016; 106(8): 1213-1241.
  5. Golsefid SMM, Zarandi MHF, Bastani S. Fuzzy duocentric community detection model in social networks. Int J Intell Syst 2015; 43(12): 177-189.
  6. Bai L, Cheng X, Liang J, Guo Y. Fast graph clustering with a new description model for community detection. Inf Sci 2017; 388-389: 37-47.
  7. Zhang H, Chen X, Li J, Zhou B. Fuzzy community detection via modularity guided membership-degree propagation. Pattern Recognit Lett 2016; 70: 66-72.
  8. Zhang H, Wu Y. Optimization and application of clustering algorithm in community discovery. Wirel Pers Commun 2018; 102(2): 1-12.
  9. Linares OAC, Botelho GM, Rodrigues FA, Neto JB. Segmentation of large images based on super-pixels and community detection in graphs. IET Image Proces 2016; 11(12): 1219-1228.
  10. Žalik KR. Community detection in networks using new update rules for label propagation. Computing 2016; 99(7): 1-22.
  11. Yin C, Zhu S, Chen H, Zhang B. A Method for community detection of complex networks based on hierarchical clustering. Int J Distrib Sens Netw 2015; 2015: 137.
  12. Chen PY, Zhang B, Hasan MA. Incremental eigenpair computation for Graph Laplacian Matrices: Theory and applications. Soc Netw Anal Min 2017; 8(1): 4.
  13. Cordeiro M, Sarmento R P, Gama J. Dynamic community detection in evolving networks using locality modularity optimization. Soc Netw Anal Min 2016; 6(1): 15.
  14. Mohan A, Venkatesan R, Pramod KV. A scalable method for link prediction in large real world networks. J Parallel Distrib Comput 2017; 109: 89-101.
  15. Konect. Source: <https://west.uni-koblenz.de/konect>.
  16. Du W, He X. A common strategy to improve community detection performance based on the nodes’ property. Commun Comput Inf Sci 2016: 355-361.
  17. Sun Y, Danila B, Josic K, Bassler KE. Improved community structure detection using a modified fine-tuning strategy. EPL 2009; 86(2): 28004.
  18. Yu H, Blair RH. A framework for attribute-based community detection with applications to integrated functional genomics. Pac Symp Biocomput 2016; 21: 69-80.
  19. Zhang X, Liu BQ, Wang XL. Research on community detection methods in complex network. CEA 2015; 13(2): 368.

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