著者

杉原 貴彦 劉 欣 村田 剛志

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.28, no.1, pp.67-76, 2013 (Released:2013-01-05)

参考文献数

18

被引用文献数

2

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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.

著者

杉原 貴彦 村田 剛志

出版者

人工知能学会

雑誌

人工知能学会全国大会論文集 (ISSN:13479881)

巻号頁・発行日

vol.25, 2011

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Freyらが提案したメッセージ交換型クラスタリング手法であるAffinity Propagationを用いてネットワークからのコミュニティ抽出を行った. メッセージとして頂点間の類似度を用いて実験を行い, いくつかのネットワークにおいては比較的高速な時間で高いモジュラリティを もつコミュニティの抽出に成功した. また,この手法が有効なネットワークの特徴についての考察も行った.