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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
JournalConcurrency Computation Practice and Experience
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • game theory
  • group behavior
  • group cohesion
  • spread model
  • uncertainty

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics

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