关键词: 信息传播预测, Transformer, 关系图卷积网络, 双向长短期记忆网络, 时空特征

Abstract: Aiming at the problem that in the dynamic evolution of information diffusion, it is difficult to effectively capture structural features, temporal features, and the interactive expression between them, an information diffusion prediction model based on Transformer and Relational Graph Convolutional Network (TRGCN) was proposed. Firstly, a dynamic heterogeneous graph composed of the social network graph and the diffusion cascade graph was constructed. The structural features of each node in this graph were then extracted using Relational Graph Convolutional Network (RGCN). Secondly, the time embedding of each node was reencoded using Bi-directional Long Short-Term Memory (Bi-LSTM). Then the time decay function was introduced to give different weights to nodes at different time positions, so as to obtain the temporal features of nodes. Finally, structural features and temporal features were input into Transformer and then merged. Finally, the spatial-temporal features were obtained for information diffusion prediction. The experimental results on three real data sets of Twitter, Douban and Memetracker show that compared with the optimal model in the comparison experiment, the TRGCN model has an average increase of 4.25% in Hits@100 metric and 12.12% in map@100 metric. The validity and rationality of the model are proved.

Key words: Keywords: information diffusion prediction, Transformer, Relational Graph Convolutional Network (RGCN), Bi-directional Long Short-Term Memory (Bi-LSTM) network, spatial-temporal feature