Modeling social network behavior spread based on group cohesion under uncertain environment

Weimin Li, Zhibin Deng, Xiaokang Zhou*, Qun Jin, Bin Sheng

*この研究の対応する著者

研究成果: Article査読

抄録

Behavior is autonomous, convergent, and uncertain, which brings challenges to the modeling of social network behavior spread. In this article, we propose a behavior spread model based on group cohesion under uncertain environments. First, for behavioral convergence, we define group cohesion to quantify the convergent effects of group. Second, based on the game theory to model the autonomy of behavior, according to the characteristics of the game payoffs changing with time and the depth of spread, and integrating group cohesion, a dynamic game payoffs calculation method is designed. Finally, aiming at the uncertainty of behavior, a group behavior spread model based on random utility theory is established. Experiments on multiple real social network behavior spread datasets demonstrate the effectiveness of the proposed model in modeling and predicting behavior spread processes under uncertain environments.

本文言語English
論文番号e7101
ジャーナルConcurrency Computation Practice and Experience
34
21
DOI
出版ステータスPublished - 2022 9月 25

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • ソフトウェア
  • 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
  • 計算理論と計算数学

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