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Minimum budget for misinformation blocking in online social networks

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Abstract

Preventing misinformation spreading has recently become a critical topic due to an explosive growth of online social networks. Instead of focusing on blocking misinformation with a given budget as usually studied in the literatures, we aim to find the smallest set of nodes (minimize the budget) whose removal from a social network reduces the influence of misinformation (influence reduction) greater than a given threshold, called the Targeted Misinformation Blocking problem. We show that this problem is #P-hard under Linear Threshold and NP-hard under Independent Cascade diffusion models. We then propose several efficient algorithms, including approximation and heuristic algorithms to solve the problem. Experiments on real-world network topologies show the effectiveness and scalability of our algorithms that outperform other state-of-the-art methods.

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Notes

  1. https://snap.stanford.edu/data/index.html.

  2. http://www.arXiv.org.

  3. Instructions for use https://networkx.github.io/.

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Authors and Affiliations

  1. University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam

    Canh V. Pham & Huan X. Hoang

  2. Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611, USA

    My T. Thai

  3. People’s Security Academy, Hanoi, Vietnam

    Canh V. Pham & Quat V. Phu

  4. School of Management, Hefei University of Technology, Hefei, China

    Jun Pei

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  1. Canh V. Pham
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  2. Quat V. Phu
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  3. Huan X. Hoang
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  4. Jun Pei
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  5. My T. Thai
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Correspondence to Huan X. Hoang or My T. Thai.

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Pham, C.V., Phu, Q.V., Hoang, H.X. et al. Minimum budget for misinformation blocking in online social networks. J Comb Optim 38, 1101–1127 (2019). https://doi.org/10.1007/s10878-019-00439-5

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