TESA: A Trajectory and Semantic-aware Dynamic Heterogeneous Graph Neural Network

Dynamic graph neural networks (DGNNs) are designed to capture the dynamic evolution of graph node interactions. However, existing DGNNs mainly consider homogeneous graphs, neglecting the rich heterogeneity in node and edge types, which is prevalent for real-world graphs and essential for modeling complex dynamic interactions. In this work, we propose the TrajEctory and Semantic-Aware dynamic heterogeneous graph neural network (TeSa), which integrates trajectory-based evolution and semantic-aware aggregation to capture both the evolving dynamics and heterogeneous semantics entailed in continuous-time dynamic heterogeneous graphs. In particular, trajectory-based evolution treats the interactions received by each node (called node trajectory) as a sequence and employs a temporal point process to learn the dynamic evolution in these interactions. Semantic-aware aggregation separates edges of different types when aggregating messages for each node from its neighbors. Edges of the same type are processed at first (i.e., intra-semantic aggregation), and then edges of different types are handled (i.e., inter-semantic fusion), to offer a comprehensive view of the heterogeneous semantics. We compare TeSa with 7 state-of-the-art DGNN models, and the results show that TeSa improves the best-performing baseline by an average of 5.11% and 5.74% in accuracy for transductive and inductive tasks.