Influence Distribution for Misinformation Containment Under Competitive Activation Models

The widespread adoption of social networks facilitates the dissemination of authentic information while also accelerating the spread of misinformation, such as rumors. The propagation of positive information can enhance user awareness and mitigate the hazards of misinformation. The misinformation containment (MC) problem aims to identify a set o <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> nodes that initiate the spread of positive information, maximizing its influence while minimizing the hazards of misinformation. The greedy approach, which employs extensive Monte Carlo simulations to estimate influence, is time-consuming and can only prioritize either propagation or containment, but not both. This paper studies the MC problem under competitive activation models. Based on geometric models of probability, we calculate the approximate probabilities of nodes being activated by positive information and misinformation at various times. Taking into account the two-hop theory, we propose a consistent and efficient computational method to assess node influence distribution from the perspectives of propagation and containment. This method strikes a balance between propagation and containment, surpassing degree centrality, further informing a heuristic solution to the MC problem. The heuristic solution's overall performance surpasses that of greedy approaches, which can only prioritize one aspect. Experiments on real-world networks demonstrate that our approach effectively balances the propagation of positive information and misinformation containment with low time complexity.