Ranking learning entities on the web by integrating network-based features

Yingzi Jin, Yutaka Matsuo, Mitsuru Ishizuka

研究成果: Chapter

抜粋

Many efforts are undertaken by people and companies to improve their popularity, growth, and power, the outcomes of which are all expressed as rankings (designated as target rankings). Are these rankings merely the results of those person's or that company's own attributes? In the theory of social network analysis (SNA), the performance and power of actors are usually interpreted as relations and the relational structures in which they are embedded.We propose an algorithm to generate and integrate network-based features systematically from a given social network that is mined from the world-wide web. After learning a model for explaining target rankings researchers' productivity based on social networks confirms the effectiveness of our models. This chapter specifically examines the application of a social network that exemplifies the advanced use of social networks mined from the web.

元の言語English
ホスト出版物のタイトルStudies in Computational Intelligence
ページ107-123
ページ数17
288
DOI
出版物ステータスPublished - 2010
外部発表Yes

出版物シリーズ

名前Studies in Computational Intelligence
288
ISSN(印刷物)1860949X

ASJC Scopus subject areas

  • Artificial Intelligence

フィンガープリント Ranking learning entities on the web by integrating network-based features' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Jin, Y., Matsuo, Y., & Ishizuka, M. (2010). Ranking learning entities on the web by integrating network-based features. : Studies in Computational Intelligence (巻 288, pp. 107-123). (Studies in Computational Intelligence; 巻数 288). https://doi.org/10.1007/978-3-642-13422-7_7