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


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.


Common Neighbors;Community Discovery;Similarity;Weighted Networks


Supported by : Northeast Petroleum University, Heilongjiang Natural Science Foundation


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