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Community Discovery in Weighted Networks Based on the Similarity of Common Neighbors

  • Liu, Miaomiao (Northeast Petroleum University) ;
  • Guo, Jingfeng (College of Information Science and Engineering, Yanshan University) ;
  • Chen, Jing (College of Information Science and Engineering, Yanshan University)
  • Received : 2017.03.03
  • Accepted : 2017.05.12
  • Published : 2019.10.31

Abstract

In view of the deficiencies of existing weighted similarity indexes, a hierarchical clustering method initialize-expand-merge (IEM) is proposed based on the similarity of common neighbors for community discovery in weighted networks. Firstly, the similarity of the node pair is defined based on the attributes of their common neighbors. Secondly, the most closely related nodes are fast clustered according to their similarity to form initial communities and expand the communities. Finally, communities are merged through maximizing the modularity so as to optimize division results. Experiments are carried out on many weighted networks, which have verified the effectiveness of the proposed algorithm. And results show that IEM is superior to weighted common neighbor (CN), weighted Adamic-Adar (AA) and weighted resources allocation (RA) when using the weighted modularity as evaluation index. Moreover, the proposed algorithm can achieve more reasonable community division for weighted networks compared with cluster-recluster-merge-algorithm (CRMA) algorithm.

Keywords

Common Neighbors;Community Discovery;Similarity;Weighted Networks

Acknowledgement

Supported by : Northeast Petroleum University, Heilongjiang Natural Science Foundation

References

  1. M. E. J. Newman, "Analysis of weighted networks," Physical Review E, vol. 70, no. 5, article no. 056131, 2004.
  2. K. Subramani, A. Velkov, I. Ntoutsi, P. Kroger, and H. P. Kriegel, "Density-based community detection in social networks," in Proceedings of 2011 IEEE 5th International Conference on Internet Multimedia Systems Architecture and Application, Bangalore, India, 2011, pp. 1-8.
  3. R. Liu, S. Feng, R. Shi, and W. Guo, "Weighted graph clustering for community detection of large social networks," Procedia Computer Science, vol. 31, pp. 85-94, 2014. https://doi.org/10.1016/j.procs.2014.05.248
  4. T. Sharma, "Finding communities in weighted signed social networks," in Proceedings of 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Istanbul, Turkey, 2012, pp. 978-982.
  5. Z. Lu, Y. Wen, and G. Cao, "Community detection in weighted networks: algorithms and applications," in Proceedings of 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), San Diego, CA, 2013, pp. 179-184.
  6. K. Wang, G. H. Lv, Z. W. Liang, and M. Y. Ye, "Detecting community in weighted complex network based on similarities," Journal of Sichuan University (Natural Science Edition), vol. 51, no. 6, pp. 1170-1176, 2014.
  7. W. Q. Lin, F. S. Lu, Z. Y. Ding, Q. Y. Wu, B. Zhou, and Y. Jia, "Parallel computing hierarchical community approach based on weighted-graph," Journal of Software, vol. 6, no. 23, pp. 1517-1530, 2012.
  8. S. Wang, "Community detection based on the interaction modularity on weighted graphs," Yunnan University, Kunming, China, 2014.
  9. P. Zhan, "Implementation of parallelized method for local community detection in weighted complex networks," South China University of Technology, Guangzhou, China, 2013.
  10. J. Zhao and J. An, "Community detection algorithm for directed and weighted network," Application Research of Computers, vol. 31, no. 12, pp. 3795-3799, 2014.
  11. Z. Yao, "The analysis and prediction of weighted complex networks," Qingdao Technological University, Qingdao, China, 2012.
  12. J. Guo, M. Liu, L. Liu, and X. Chen, "An improved community discovery algorithm in weighted social networks," ICIC Express Letters, vol. 10, no. 1, pp. 35-41, 2016.
  13. X. Liu, "Community structure detection in complex networks via objective function optimization," National University of Defense Technology, Changsha, China, 2012.