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

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1962))

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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 small number of clusters. In this context, designing the communication topology of the social network in a distributed manner is a 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 process, 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.

S. Guo and H. Xu–Equal contribution.

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Acknowledgements

This work is partially supported by National Natural Science Foundation of China, Grant Nos. 62006047 and 618760439, Guangdong Natural Science Foundation, Grant No. 2021B0101220004.

Author information

Authors and Affiliations

  1. School of Computer Science and Technological, Guangdong University of Technology, No. 100 Waihuanxi Road, HEMC, Guangzhou, 510006, Guangdong, China

    Shijun Guo & Guangqiang Xie

  2. Peng Cheng Laboratory, Xingke 1st Street, Shenzhen, 518066, Guangdong, China

    Haoran Xu & Di Wen

  3. School of Intelligent Systems Engineering, Sun Yat-sen University, No. 66 Gongchang Road, Shenzhen, 510275, Guangdong, China

    Haoran Xu, Di Wen & Peixi Peng

  4. Department of Computer Science and Technology, Peking University, Beijing, 100871, China

    Yangru Huang & Peixi Peng

Authors
  1. Shijun Guo
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  2. Haoran Xu
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  3. Guangqiang Xie
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  4. Di Wen
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  5. Yangru Huang
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Corresponding author

Correspondence to Guangqiang Xie .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Guo, S., Xu, H., Xie, G., Wen, D., Huang, Y., Peng, P. (2024). Reinforcement Learning-Based Consensus Reaching in Large-Scale Social Networks. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1962. Springer, Singapore. https://doi.org/10.1007/978-981-99-8132-8_13

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  • DOI: https://doi.org/10.1007/978-981-99-8132-8_13

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  • Online ISBN: 978-981-99-8132-8

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