Uncertainty Rumor Blocking in Social Networks: A Graph Inverse Reinforcement Learning Approach

Qiang He, Zhen Tang, Runze Jiang, Zelin Zhang, Hui Fang, Xingwei Wang, Lianbo Ma, Keping Yu

Abstract: Rumor blocking approaches in social networks aim to identify a small set of counter-rumor seed nodes and compete with rumor cascades to quickly stop the propagation of rumors. However, current rumor blocking methods assume complete knowledge of rumor node positions, which is often unattainable in real-world scenarios. In this paper, we introduce the concept of Uncertainty Rumor Blocking, where we address the uncertainty surrounding rumor node locations by considering a set of suspicious nodes, each associated with a probability indicating the likelihood of rumor propagation. As traditional node selection algorithms become inadequate under uncertain conditions, we propose a Graph Neural Network-based Inverse Reinforcement Learning (G-IRL) approach to effectively select counter-rumor seed nodes. Through comprehensive experimentation on three datasets, we demonstrate the consistent superiority of our G-IRL over state-of-the-art baseline methods for node selection in the context of uncertainty rumor containment.

External IDs:doi:10.1109/ton.2026.3659336