Community Detection from Signed Networks
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- Sugihara Takahiko
- Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology
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- Liu Xin
- Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology JST CREST
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- Murata Tsuyoshi
- Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology
Bibliographic Information
- Other Title
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- Signedネットワークからのコミュニティ抽出
Abstract
Many real-world complex systems can be modeled as networks, and most of them exhibit community structures. Community detection from networks is one of the important topics in link mining. In order to evaluate the goodness of detected communities, Newman modularity is widely used. In real world, however, many complex systems can be modeled as signed networks composed of positive and negative edges. Community detection from signed networks is not an easy task, because the conventional detection methods for normal networks cannot be applied directly. In this paper, we extend Newman modularity for signed networks. We also propose a method for optimizing our modularity, which is an efficient hierarchical agglomeration algorithm for detecting communities from signed networks. Our method enables us to detect communities from large scale real-world signed networks which represent relationship between users on websites such as Wikipedia, Slashdot and Epinions.
Journal
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- Transactions of the Japanese Society for Artificial Intelligence
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Transactions of the Japanese Society for Artificial Intelligence 28 (1), 67-76, 2013
The Japanese Society for Artificial Intelligence
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Keywords
Details 詳細情報について
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- CRID
- 1390282680084454784
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- NII Article ID
- 130003362308
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- BIBCODE
- 2013TJSAI..28...67S
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- ISSN
- 13468030
- 13460714
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed