Multiple Rumor Source Recognition Model Based on Graph Attention Networks

The accurate recognition of rumor sources can help suppress the spread of rumors and reduce their impact on the public. Existing rumor source recognition models often overlook the differences in mutual influence between nodes, which leads to equal weighting when aggregating neighboring feature information, thereby reducing the accuracy of rumor source recognition. This paper proposes a multiple-rumor source recognition model based on Graph Attention Networks (GATs), called, MRSDGAT. First, in a social network where a rumor has already spread, user status, source prominence of rumors, and centrality are used to represent user nodes as vectors that are then used to construct a feature matrix for the nodes. Subsequently, using the GAT is employed to explore the mutual influence between nodes, calculate the influence weights, and aggregate node feature information according to the weight of the influence between nodes. Simultaneously, residual connections are introduced between the attention layers to resolve the issue of gradient disappearance and improve the ability to identify multiple rumor sources. Finally, the model outputs the probability value of each node as a source node. The larger the probability value, the greater the possibility that the node is a source node. The experimental results show that on the Karate dataset, the F1 value of the MRSDGAT model improves by 14.09, 13.32, and 13.10 percentage points compared to the baseline GCNSI model, and by 23.41, 22.59, and 24.21 percentage points compared to the baseline LPSI model, indicating better recognition performance.