• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1055-1067
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• 2019

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.

• KIISE Transactions on Computing Practices
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• v.24 no.3
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• pp.145-150
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• 2018

Forwarding in opportunistic networks shows low performance because there may be no connecting paths between the source and the destination nodes due to the intermittent connectivity. Currently, social network analysis has been researched. Specifically, similarity is one of methods of social networks analysis. In this paper, we propose forwarding schemes based on representative similarities, and evaluate how much the forwarding performance increases. As a result, since the forwarding schemes are based on similarities, these schemes only forward messages to nodes with higher similarity as relay nodes, toward the destination node. These schemes have low network traffic and hop count while having stable transmission delay.