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Community-Based Rumor Blocking Maximization in Social Networks

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Algorithmic Aspects in Information and Management (AAIM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12290))

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Abstract

Even though the widespread use of social networks brings a lot of convenience to people’s life, it also cause a lot of negative effects. The spread of misinformation in social networks would lead to public panic and even serious economic or political crisis. We study the community-based rumor blocking problem to select b seed users as protectors such that expected number of users eventually not being influenced by rumor sources is maximized, called Community-based Rumor Blocking Maximization Problem (CRBMP). We consider the community structure in the social network and solve our problem in two stages, in the first stage, we allocate budget b for all the communities with the technique of submodular function maximization on an integer lattice, which is different from most of the existing work with the submodular function over a set function. We prove that the objective function for the budget allocation problem is monotone and DR-submodular, then a greedy algorithm is devised to get a \(1-1/e\) approximation ratio; then we solve the Protector Seed Selection (PSS) problem in the second stage after we obtained the budget allocation vector for communities, we greedily choose protectors for each communities with the budget constraints to achieve the maximization of the influence of protectors. The greedy algorithm for PSS problem can achieve a \(\frac{1}{2}\)-approximation guarantee. At last, we verified the effectiveness and superiority of our algorithms on three real world datasets .

This work is supported by the National Natural Science Foundation of China (No. 61772385, No. 61572370) and supported partially by NSF 1907472.

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Acknowledgment

This work is supported by the National Natural Science Foundation of China (No.61772385, No.61572370) and supported partially by NSF 1907472.

Author information

Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, 430072, China

    Qiufen Ni & Chuanhe Huang

  2. Collaborative Innovation Center of Geospatial Technology, Wuhan, 430072, China

    Qiufen Ni & Chuanhe Huang

  3. Department of Computer Science, The University of Texas at Dallas, Richardson, TX, 75080, USA

    Jianxiong Guo & Weili Wu

Authors
  1. Qiufen Ni
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  2. Jianxiong Guo
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  3. Chuanhe Huang
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  4. Weili Wu
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Corresponding author

Correspondence to Chuanhe Huang .

Editor information

Editors and Affiliations

  1. Zhejiang Normal University, Jinhua, China

    Zhao Zhang

  2. Texas Southern University, Houston, TX, USA

    Wei Li

  3. University of Texas at Dallas, Richardson, TX, USA

    Ding-Zhu Du

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Ni, Q., Guo, J., Huang, C., Wu, W. (2020). Community-Based Rumor Blocking Maximization in Social Networks. In: Zhang, Z., Li, W., Du, DZ. (eds) Algorithmic Aspects in Information and Management. AAIM 2020. Lecture Notes in Computer Science(), vol 12290. Springer, Cham. https://doi.org/10.1007/978-3-030-57602-8_7

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  • DOI: https://doi.org/10.1007/978-3-030-57602-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57601-1

  • Online ISBN: 978-3-030-57602-8

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