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

Miaomiao Liu, Jingfeng Guo and Jing Chen
Volume: 15, No: 5, Page: 1055 ~ 1067, Year: 2019
10.3745/JIPS.04.0133
Keywords: Common Neighbors, Community Discovery, Similarity, Weighted Networks
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Abstract
In view of the deficiencies of existing weighted similarity indexes, a hierarchical clustering method initializeexpand- 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.

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Cite this article
IEEE Style
M. Liu, J. Guo and J. Chen, "Community Discovery in Weighted Networks Based on the Similarity of Common Neighbors," Journal of Information Processing Systems, vol. 15, no. 5, pp. 1055~1067, 2019. DOI: 10.3745/JIPS.04.0133.

ACM Style
Miaomiao Liu, Jingfeng Guo, and Jing Chen. 2019. Community Discovery in Weighted Networks Based on the Similarity of Common Neighbors, Journal of Information Processing Systems, 15, 5, (2019), 1055~1067. DOI: 10.3745/JIPS.04.0133.