Analysis of user network and correlation for community discovery based on topic-aware similarity and behavioral influence

Xiaokang Zhou, Bo Wu, Qun Jin*

*この研究の対応する著者

研究成果: Article査読

47 被引用数 (Scopus)

抄録

While social computing related research has focused mostly on how to provide users with more precise and direct information, or on recommending new search methods to find requested information rapidly, the authors believe that network users themselves could be viewed as an important social resource. This study concentrates on analyzing potential and dynamic user correlations, based on topic-aware similarity and behavioral influence, which may help us to discover communities in social networking sites. The dynamically socialized user networking (DSUN) model is extended and refined to represent implicit and explicit user relationships in terms of topic-aware features and social behaviors. A set of measures is defined to describe and quantify interuser correlations, relating to social behaviors. Three types of ties are proposed to describe and discover communities according to influence-based user relationships. Results of the experiment with Twitter data are used to show the discovery of three types of communities, based on the presented model. Comparison with six different schemes and two existing methods demonstrates that the proposed method is effective in discovering influence-based communities. Finally, the scenario-based simulation of collective decision-making processes demonstrates the practicability of the proposed model and method in social interactive systems.

本文言語English
論文番号8019836
ページ(範囲)559-571
ページ数13
ジャーナルIEEE Transactions on Human-Machine Systems
48
6
DOI
出版ステータスPublished - 2018 12

ASJC Scopus subject areas

  • 人的要因と人間工学
  • 制御およびシステム工学
  • 信号処理
  • 人間とコンピュータの相互作用
  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信
  • 人工知能

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