Community Detection in Large-scale Bipartite Networks
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概要
- 論文の詳細を見る
Community detection in networks receives much attention recently. Most of the previous works are for unipartite networks composed of only one type of nodes. In real world situations, however, there are many bipartite networks composed of two types of nodes. In this paper, we propose a fast algorithm called LP&BRIM for community detection in large-scale bipartite networks. It is based on a joint strategy of two developed algorithms — label propagation (LP), a very fast community detection algorithm, and BRIM, an algorithm for generating better community structure by recursively inducing divisions between the two types of nodes in bipartite networks. Through experiments, we demonstrate that this new algorithm successfully finds meaningful community structures in large-scale bipartite networks in reasonable time limit.
- Information and Media Technologies 編集運営会議の論文
著者
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Liu Xin
Graduate School Of Engineering Chiba University
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Murata Tsuyoshi
Graduate School of Information Science and Engineering, Tokyo Institute of Technology
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Liu Xin
Graduate School of Information Science and Engineering, Tokyo Institute of Technology
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