Minimum budget for misinformation blocking in online social networks

Canh V. Pham (), Quat V. Phu (), Huan X. Hoang (), Jun Pei () and My T. Thai ()
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Canh V. Pham: University of Engineering and Technology, Vietnam National University
Quat V. Phu: People’s Security Academy
Huan X. Hoang: University of Engineering and Technology, Vietnam National University
Jun Pei: Hefei University of Technology
My T. Thai: University of Florida

Journal of Combinatorial Optimization, 2019, vol. 38, issue 4, No 8, 1127 pages

Abstract: 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.

Keywords: Misinformation; Social network; Approximation algorithm; Optimization (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10878-019-00439-5

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