Consensus Opinion Enhancement in Social Network with Multi-agent Reinforcement Learning

Xie Guang-qiang; Xu Hao-ran; Li Yang; Chen Guang-fu

doi:10.12052/gdutxb.220042

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Consensus Opinion Enhancement in Social Network with Multi-agent Reinforcement Learning

更新时间：2026-05-27

- Consensus Opinion Enhancement in Social Network with Multi-agent Reinforcement Learning
- Vol. 39, Issue 6, Pages: 36-43(2022)
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.220042
  CLC： TP391
- Online First：10 November 2022，
  
  Published：2022
- 稿件说明：
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谢光强, 许浩然, 李杨, 陈广福. 基于多智能体强化学习的社交网络舆情增强一致性方法[J]. 广东工业大学学报, 2022, 39(6): 36-43.

Xie Guang-qiang, Xu Hao-ran, Li Yang, et al. Consensus Opinion Enhancement in Social Network with Multi-agent Reinforcement Learning[J]. 2022, 39(6): 36-43.
谢光强, 许浩然, 李杨, 陈广福. 基于多智能体强化学习的社交网络舆情增强一致性方法[J]. 广东工业大学学报, 2022, 39(6): 36-43. DOI： 10.12052/gdutxb.220042.

Xie Guang-qiang, Xu Hao-ran, Li Yang, et al. Consensus Opinion Enhancement in Social Network with Multi-agent Reinforcement Learning[J]. 2022, 39(6): 36-43. DOI： 10.12052/gdutxb.220042.

摘要

针对社交网络舆情动力学的增强一致性问题，提出了一种基于多智能体强化学习的智能感知模型(Consensus Opinion Enhancement with Intelligent Perception

COEIP) 。在舆情动力学场景下的马尔科夫决策过程中，首先通过双向循环神经网络设计了智能体的决策模型以解决智能体不定长感知的问题。然后通过差分奖励的思想针对收敛效率、连通度和通信代价三类目标，设计了有效的奖励函数。最后为优化COEIP模型，设计了基于策略梯度的多智能体探索与更新算法，让智能体在彼此交互过程中，通过奖励值自适应学习具备多目标权衡能力的邻域选择策略。大量仿真验证了COEIP在社交网络舆情动力学场景下可以有效调和智能体间的矛盾，降低系统稳定时的簇数，进而增强系统的舆情一致性。本模型为大规模社交网络下提高人群意见的统一性提供了新的解决方案，具有重要的理论指导意义。

Abstract

Aiming at the problem of consensus enhancement in opinion dynamics of social network

a consensus opinion enhancement with intelligent perception (COEIP) model based on multi-agent reinforcement learning is proposed. In the Markov decision-making process in opinion dynamics

firstly

the decision-making model of agent is designed through bidirectional recurrent neural network to solve the problem of uncertain-length perception. Then

through the idea of difference reward

an effective reward function is designed for the three objectives of convergence efficiency

connectivity and communication cost. Finally

in order to optimize COEIP model

a multi-agent exploration and collaborative update algorithm based on policy gradient is designed

which can enable agents to adaptively learn the neighborhood selection strategy with multi-objective trade-off ability through the reward value in the process of interaction with each other. A large number of simulations verify that COEIP can effectively reconcile the contradictions between agents and reduce the number of clusters when the system is stable in the scenario of opinion dynamics of social network

thus enhancing the consensus opinion of the system. This model provides a new solution for improving the unity of people's opinions under large-scale social networks

which has important theoretical guiding significance.

关键词

Keywords

references

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