Reinforcement Learning-Based Consensus-Reaching in Large-Scale Social Networks

EasyChair Preprint no. 11382

Abstract

Social networks in present-day industrial environments encompass a wide range of personal information that has significant research and application potential. One notable challenge in the domain of opinion dynamics of social networks is achieving convergence of opinions to a limited number of clusters. In this context, designing the communication topology of the social network in a distributed manner is particularly difficult. To address this problem, this paper proposes a novel perception model for agents. The proposed model, which is based on bidirectional recurrent neural networks, can adaptively reweight the influence of perceived neighbors in the convergence process of opinion dynamics. Additionally, effective differential reward functions are designed to optimize three objectives: convergence degree, connectivity, and cost of convergence. Lastly, a multi-agent exploration and exploitation algorithm based on policy gradient is designed to optimize the model. Based on the reward values in inter-agent interaction processes, the agents can adaptively learn the neighbor reweighting strategy with multi-objective trade-off abilities. Extensive simulations demonstrate that the proposed method can effectively reconcile conflicting opinions among agents and accelerate convergence.

Keyphrases: opinion dynamics, Reinforcement Learning, Reweighting perception, social network

@Booklet{EasyChair:11382,
  author = {Shijun Guo and Haoran Xu and Guangqiang Xie and Di Wen and Yangru Huang and Peixi Peng},
  title = {Reinforcement Learning-Based Consensus-Reaching in Large-Scale Social Networks},
  howpublished = {EasyChair Preprint no. 11382},

  year = {EasyChair, 2023}}