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- XIN LIU
- Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo 152-8552, Japan
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- WEICHU LIU
- Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro, Tokyo 152-8852, Japan
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- TSUYOSHI MURATA
- Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro, Tokyo 152-8852, Japan
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- KEN WAKITA
- Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo 152-8552, Japan
抄録
<jats:p> There has been a surge of interest in community detection in homogeneous single-relational networks which contain only one type of nodes and edges. However, many real-world systems are naturally described as heterogeneous multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose a new method for detecting communities in such networks. Our method is based on optimizing the composite modularity, which is a new modularity proposed for evaluating partitions of a heterogeneous multi-relational network into communities. Our method is parameter-free, scalable, and suitable for various networks with general structure. We demonstrate that it outperforms the state-of-the-art techniques in detecting pre-planted communities in synthetic networks. Applied to a real-world Digg network, it successfully detects meaningful communities. </jats:p>
収録刊行物
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- Advances in Complex Systems
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Advances in Complex Systems 17 (06), 1450018-, 2014-11
World Scientific Pub Co Pte Lt
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詳細情報 詳細情報について
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- CRID
- 1360567186124228096
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- ISSN
- 17936802
- 02195259
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- データソース種別
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- Crossref
- KAKEN