Generating useful network-based features for analyzing social networks

Jun Karamon, Yutaka Matsuo, Mitsuru Ishizuka

研究成果: Conference contribution

12 引用 (Scopus)

抜粋

Recently, many Web services such as social networking services, blogs, and collaborative tagging have become widely popular. Many attempts are being made to investigate user interactions by analyzing social networks among users. However, analyzing a social network with attributional data is often not an easy task because numerous ways exist to define features through aggregation of different tables. In this study, we propose an algorithm to identify important network-based features systematically from a given social network to analyze user behavior efficiently and to expand the services. We apply our method for link-based classification and link prediction tasks with two different datasets, i.e., an @cosme (an online viral marketing site) dataset and a Hatena Bookmark (collaborative tagging service) dataset, to demonstrate the usefulness of our algorithm. Our algorithm is general and can provide useful network-based features for social network analyses.

元の言語English
ホスト出版物のタイトルProceedings of the National Conference on Artificial Intelligence
ページ1162-1168
ページ数7
2
出版物ステータスPublished - 2008
外部発表Yes
イベント23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08 - Chicago, IL
継続期間: 2008 7 132008 7 17

Other

Other23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08
Chicago, IL
期間08/7/1308/7/17

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • これを引用

    Karamon, J., Matsuo, Y., & Ishizuka, M. (2008). Generating useful network-based features for analyzing social networks. : Proceedings of the National Conference on Artificial Intelligence (巻 2, pp. 1162-1168)