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http://dx.doi.org/10.3745/KTSDE.2015.4.1.37

An Efficient Algorithm for Betweenness Centrality Estimation in Social Networks  

Shin, Soo-Jin (한국기술교육대학교 컴퓨터공학부)
Kim, Yong-Hwan (한국기술교육대학교 컴퓨터공학부)
Kim, Chan-Myung (한국기술교육대학교 컴퓨터공학부)
Han, Youn-Hee (한국기술교육대학교 컴퓨터공학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.4, no.1, 2015 , pp. 37-44 More about this Journal
Abstract
In traditional social network analysis, the betweenness centrality measure has been heavily used to identify the relative importance of nodes. Since the time complexity to calculate the betweenness centrality is very high, however, it is difficult to get it of each node in large-scale social network where there are so many nodes and edges. In our past study, we defined a new type of network, called the expanded ego network, which is built only with each node's local information, i.e., neighbor information of the node's neighbor nodes, and also defined a new measure, called the expanded ego betweenness centrality. In this paper, We propose algorithm that quickly computes expanded ego betweenness centrality by exploiting structural properties of expanded ego network. Through the experiment with virtual network used Barab$\acute{a}$si-Albert network model to represent the generic social network and facebook network to represent actual social network, We show that the node's importance rank based on the expanded ego betweenness centrality has high similarity with that the node's importance rank based on the existing betweenness centrality. We also show that the proposed algorithm computes the expanded ego betweenness centrality quickly than existing algorithm.
Keywords
Social Network Analysis; Betweenness Centrality; Local Information; Expanded Ego Network; Expanded Ego Betweenness Centrality; Algorithm;
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Times Cited By KSCI : 2  (Citation Analysis)
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