关键词: 谣言影响力最小化, 图神经网络, Double DQN

Abstract: The spread of rumors is rapid and poses significant harm. Thus, utilizing fact-checking announcement to curb the dissemination of rumors is of great importance for social stability. To minimize and eliminate the influence of already disseminated rumors as soon as possible, a dynamic rumor refutation model named GCNNs-DDQN, combining graph convolutional networks (GCN), graph neural networks (GNN), and double deep Q-network (Double DQN), is proposed. Firstly, GCN is used to obtain the initial node embeddings for both source nodes and target nodes. These embeddings, combined with node state vectors, serve as inputs to four coupled GNNs to derive complex node embeddings. Subsequently, Q-value computation is performed, and Double DQN is employed to optimize the weight parameters of the four coupled GNNs and the Double DQN model using Q-values and rewards. This approach enables the selection of the most influential nodes to release fact-checking announcement based on the current state of rumor propagation. 10 real-world datasets of varying sizes are used to evaluate the rumor refutation effectiveness of the model, and an analysis is conducted on the impact of various parameters on the rumor refutation performance. An ablation experiment is also conducted. Experimental results demonstrate that the GCNNs-DDQN model possesses stronger generalization capabilities and can be applied to different social networks. Compared with traditional algorithms, it can reduce the rumor refutation time by up to two-unit time. An increase in parameter values that favor rumors will make it more difficult to counteract the rumors and prolong the time needed for their suppression. Conversely, factors that favor fact-checking announcement may not necessarily reduce the suppression time; instead, they can potentially hinder the effort to counteract rumors.

Key words: rumor influence minimization, graph neural network, Double DQN