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

2026-01-29

PRE

Qiang He

Zhen Tang

Runze Jiang

Zelin Zhang

Hui Fang

Xingwei Wang

Lianbo Ma

Keping Yu

DOI：10.1109/TON.2026.3659336

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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.

Keywords:

Social networks

rumor blocking

information propagation

inverse reinforcement learning

graph neural network

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

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