Community Discovery in Weighted Networks Based on the Similarity of Common Neighbors
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초록
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 inweighted networks. Firstly, the similarity of the node pair is defined based on the attributes of their commonneighbors. Secondly, the most closely related nodes are fast clustered according to their similarity to form initialcommunities and expand the communities. Finally, communities are merged through maximizing themodularity so as to optimize division results. Experiments are carried out on many weighted networks, whichhave verified the effectiveness of the proposed algorithm. And results show that IEM is superior to weightedcommon neighbor (CN), weighted Adamic-Adar (AA) and weighted resources allocation (RA) when usingthe weighted modularity as evaluation index. Moreover, the proposed algorithm can achieve more reasonablecommunity division for weighted networks compared with cluster-recluster-merge-algorithm (CRMA)algorithm.
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 inweighted networks. Firstly, the similarity of the node pair is defined based on the attributes of their commonneighbors. Secondly, the most closely related nodes are fast clustered according to their similarity to form initialcommunities and expand the communities. Finally, communities are merged through maximizing themodularity so as to optimize division results. Experiments are carried out on many weighted networks, whichhave verified the effectiveness of the proposed algorithm. And results show that IEM is superior to weightedcommon neighbor (CN), weighted Adamic-Adar (AA) and weighted resources allocation (RA) when usingthe weighted modularity as evaluation index. Moreover, the proposed algorithm can achieve more reasonablecommunity division for weighted networks compared with cluster-recluster-merge-algorithm (CRMA)algorithm.
참고문헌 (13)
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