Deadline-aware misinformation prevention in social networks with time-decaying influence

• Published in: Expert systems with applications Vol. 238; p. 121847
• Main Authors: Yang, Lan, Li, Zhiwu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.03.2024
• Subjects: Independent cascade model; Misinformation prevention; Sampling-based approach; Social networks; Social networks; Misinformation prevention; Sampling-based approach; Independent cascade model
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.121847

A misinformation prevention problem is essential in social networks since misinformation could greatly mislead people and interfere societal, economical, or even political circumstances. Traditional diffusion models for describing information spread assume that active individuals have only one trial to activate their friends. This may oversimplify the mechanism of the influence between two individuals in reality, i.e., an individual can have more than one chance to influence its neighbors. In addition, the influence between nodes is considered as a constant, although it is inherently dynamic in nature. This paper addresses the deadline-aware misinformation prevention problem considering a more realistic diffusion model, i.e., competitive dynamic independent cascade model (CDIC), which allows multiple activation trials with time-decaying influence power. We further show that the CDIC preserves the monotone and submodular properties. The deadline-aware misinformation prevention problem aims to find some influential truth spreaders such that the expected number of nodes to be saved or protected from misinformation is maximized before a given deadline. An adaptive sampling approach (ASA) is proposed under the CDIC model for solving the problem, which outperforms the greedy approach with lazy evaluations by several orders of magnitude in terms of the computational efficiency through a series of simulation on real datasets. •A more realistic model for competitive diffusion (CDIC) is presented.•The DMP problem is formalized and analyzed for the CDIC model.•A scalable and approximate algorithm is proposed for solving the DMP problem.•A series of experiments are conducted to show the effectiveness of the algorithms.