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Least Cost Rumor Influence Minimization in Multiplex Social Networks

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11306))

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Abstract

This paper deals with the issue of rumors propagation in online social networks (OSNs) that are connected through overlapping users, named multiplex OSNs. We consider a strategy to initiate an anti-rumor campaign to raise the awareness of individuals and prevent the adoption of the rumor for further limiting its influence. Therefore, we introduce the Least Cost Anti-rumor Campaign (LCAC) problem to minimize the influence of the rumor. The proposed problem defines the minimum number of users to initiate this campaign, which reaches a large number of overlapping users to increase the awareness of individuals across networks. Due to the NP-hardness of LCAC problem, we prove that its objective function is submodular and monotone. Then, we introduce a greedy algorithm for LCAC problem that guarantees an approximation within \((1-1/\textit{e})\) of the optimal solution. Finally, experiments on real-world and synthetics multiplex networks are conducted to investigate the effect of the number of the overlapping users as well as the networks structure topology. The results provide evidence about the efficacy of the proposed algorithm to limit the spread of a rumor.

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Notes

  1. 1.

    www.weibo.com.

  2. 2.

    www.pewresearch.org.

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Acknowledgment

The Research was supported in part by National Basic Research Program of China (973 Program, No. 2013CB329605).

Author information

Authors and Affiliations

  1. School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China

    Adil Imad Eddine Hosni, Kan Li, Cangfeng Ding & Sadique Ahmed

  2. Ecole Militaire Polytechnique, BP 17, 16046, Algiers, Algeria

    Adil Imad Eddine Hosni

  3. School of Mathematics and Computer Science, Yanan university, Yan’an, China

    Cangfeng Ding

Authors
  1. Adil Imad Eddine Hosni
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  2. Kan Li
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  3. Cangfeng Ding
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  4. Sadique Ahmed
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Corresponding author

Correspondence to Kan Li .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Hosni, A.I.E., Li, K., Ding, C., Ahmed, S. (2018). Least Cost Rumor Influence Minimization in Multiplex Social Networks. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_9

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  • DOI: https://doi.org/10.1007/978-3-030-04224-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04223-3

  • Online ISBN: 978-3-030-04224-0

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