Stop Rumors Fast: A Multi-Objective Blocking Approach with Deep Reinforcement Learning in Social Networks

The rumor blocking approaches in social networks aim to identify a small set of counter-rumor seed nodes to quickly stop the propagation of rumors. Existing solutions predominantly focus on minimizing the influence of rumor propagation (i.e., the number of affected nodes), while treating transmission time as a constraint rather than an optimization objective, despite its critical importance in real-world scenarios. In this view, we firstly elaborate on a multi-objective rumor blocking optimization problem, which strives to simultaneously minimize the influence of rumor propagation and the transmission time, that is, to stop the rumor propagation in the shortest possible time. Then, we further propose a Continuous-Time Competitive Cascading (CTCC) model, which, adapted from the independent cascading model, introduces the concept of transmission time and the competitions between nodes, and characterizes the influence process of different nodes as a continuous process. Besides, to tackle the multi-objective rumor blocking problem, we design a Reinforcement Learning approach with Graph Convolution network (denoted as RLGC) to effectively select counter-rumor seed nodes. Extensive experimental results on three real datasets validate that RLGC can consistently perform better than representative, state-of-the-art baselines regarding both the two objectives (i.e., rumor influence strength and transmission time).