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Influence maximization of informed agents in social networks

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  • AskariSichani, Omid
  • Jalili, Mahdi

Abstract

Control of collective behavior is one of the most desirable goals in many applications related to social networks analysis and mining. In this work we propose a simple yet effective algorithm to control opinion formation in complex networks. We aim at finding the best spreaders whose connection to a reasonable number of informed agents results in the best performance. We consider an extended version of the bounded confidence model in which the uncertainty of each agent is adaptively controlled by the network. A number of informed agents with the desired opinion value is added to the network and create links with other agents such that large portion of the network follows their opinions. We propose to connect the informed agents to nodes with small in-degrees and high out-degree that are connected to high in-degree nodes. Our experimental results on both model and real social networks show superior performance of the proposed method over the state-of-the-art heuristic methods in the facet of opinion formation models.

Suggested Citation

  • AskariSichani, Omid & Jalili, Mahdi, 2015. "Influence maximization of informed agents in social networks," Applied Mathematics and Computation, Elsevier, vol. 254(C), pages 229-239.
    • Handle: RePEc:eee:apmaco:v:254:y:2015:i:c:p:229-239
    DOI: 10.1016/j.amc.2014.12.139
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    Cited by:

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    3. Yan, Fuhan & Li, Zhaofeng & Jiang, Yichuan, 2016. "Controllable uncertain opinion diffusion under confidence bound and unpredicted diffusion probability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 85-100.
    4. Wang, Shaoli & Rong, Libin & Wu, Jianhong, 2016. "Bistability and multistability in opinion dynamics models," Applied Mathematics and Computation, Elsevier, vol. 289(C), pages 388-395.
    5. Fan, Kangqi & Pedrycz, Witold, 2017. "Evolution of public opinions in closed societies influenced by broadcast media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 53-66.
    6. Xu, Yuxin & Gao, Fei, 2024. "A novel higher-order Deffuant–Weisbuch networks model incorporating the Susceptible Infected Recovered framework," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    7. Evangelos Ioannidis & Nikos Varsakelis & Ioannis Antoniou, 2020. "Promoters versus Adversaries of Change: Agent-Based Modeling of Organizational Conflict in Co-Evolving Networks," Mathematics, MDPI, vol. 8(12), pages 1-25, December.
    8. Dimitris Tsintsaris & Milan Tsompanoglou & Evangelos Ioannidis, 2024. "Dynamics of Social Influence and Knowledge in Networks: Sociophysics Models and Applications in Social Trading, Behavioral Finance and Business," Mathematics, MDPI, vol. 12(8), pages 1-27, April.
    9. He, Qiang & Wang, Xingwei & Lei, Zhencheng & Huang, Min & Cai, Yuliang & Ma, Lianbo, 2019. "TIFIM: A Two-stage Iterative Framework for Influence Maximization in Social Networks," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 338-352.
    10. Han, Wenchen & Feng, Yuee & Qian, Xiaolan & Yang, Qihui & Huang, Changwei, 2020. "Clusters and the entropy in opinion dynamics on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    11. Liu, Panfeng & Li, Longjie & Fang, Shiyu & Yao, Yukai, 2021. "Identifying influential nodes in social networks: A voting approach," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    12. Ullah, Farman & Lee, Sungchang, 2017. "Identification of influential nodes based on temporal-aware modeling of multi-hop neighbor interactions for influence spread maximization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 968-985.
    13. Fan, Kangqi & Pedrycz, Witold, 2016. "Opinion evolution influenced by informed agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 431-441.

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