Maximizing misinformation restriction within time and budget constraints

Canh V. Pham (), My T. Thai (), Hieu V. Duong (), Bao Q. Bui () and Huan X. Hoang ()
Additional contact information
Canh V. Pham: University of Engineering and Technology, Vietnam National University
My T. Thai: Ton Duc Thang University
Hieu V. Duong: People’s Security Academy Hanoi
Bao Q. Bui: People’s Security Academy Hanoi
Huan X. Hoang: University of Engineering and Technology, Vietnam National University

Journal of Combinatorial Optimization, 2018, vol. 35, issue 4, No 11, 1202-1240

Abstract: Abstract Online social networks have become popular media worldwide. However, they also allow rapid dissemination of misinformation causing negative impacts to users. With a source of misinformation, the longer the misinformation spreads, the greater the number of affected users will be. Therefore, it is necessary to prevent the spread of misinformation in a specific time period. In this paper, we propose maximizing misinformation restriction ( $$\mathsf {MMR}$$ MMR ) problem with the purpose of finding a set of nodes whose removal from a social network maximizes the influence reduction from the source of misinformation within time and budget constraints. We demonstrate that the $$\mathsf {MMR}$$ MMR problem is NP-hard even in the case where the network is a rooted tree at a single misinformation node and show that the calculating objective function is #P-hard. We also prove that objective function is monotone and submodular. Based on that, we propose an $$1{-}1/\sqrt{e}$$ 1 - 1 / e -approximation algorithm. We further design efficient heuristic algorithms, named $$\mathsf {PR}$$ PR - $$\mathsf {DAG}$$ DAG to show $$\mathsf {MMR}$$ MMR in very large-scale networks.

Keywords: Approximation algorithm; Social networks; Misinformation; Information diffusion (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10878-018-0252-3

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