Information Propagation Prediction Based on Spatial–Temporal Attention and Heterogeneous Graph Convolutional Networks

With the development of deep learning and other technologies, the research of information propagation prediction has also achieved important research achievements. However, the existing information diffusion studies either focus on the attention relationships of users or they predict the information according to the diffusion relationships of users, which makes the prediction results have certain limitations. Therefore, a prediction model has been proposed spatial–temporal attention heterogeneous graph convolutional networks (STAHGCNs). First, we use GCN to learn user influence relationships and user behavior relationships, and we propose a user representation fusion mechanism to learn the user characteristics. Second, to account for the dynamics of user behavior, a temporal attention mechanism strategy is used to encode time into the heterogeneous graph to obtain a more expressive user representation. Finally, the obtained user representation is input into the multihead attention mechanism for information propagation prediction. Experimental results performed on the Twitter, Douban, Digg, and Memetracker datasets have shown that the proposed STAHGCN model increased by 8.80% and 6.74% at hits@N and map@N, respectively, which are significantly better than the original latest DyHGCN model. The proposed STAHGCN model effectively integrates spatial factors, such as time factor, user influence, and behavior, which greatly improves the accuracy of information propagation prediction and has great significance for rumor monitoring and malicious account detection.