Hawkes Point Process-enhanced Dynamic Graph Neural Network

Dynamic graph representation learning aims to capture the evolution of graph structures and obtain accurate node embeddings, a crucial task in graph machine learning. The Hawkes point process, a mathematical framework effective for modeling the influence of historical events on future occurrences, has been validated as a powerful tool for capturing the dynamics of graph evolution in dynamic graph representation learning. However, existing dynamic graph representation learning methods based on the Hawkes point process primarily model excitation at the individual node level, failing to adequately account for structural influences during graph evolution. This limitation restricts their ability to comprehensively capture network evolution patterns. To address this limitation, we propose a Hawkes Point Process-enhanced Dynamic Graph Neural Network (HP-DGNN) model. This model leverages the Hawkes point process to model both individual node histories and structural histories, capturing their respective influences on future node interactions. By integrating individual and structural influences in computing Hawkes conditional intensity, the model comprehensively captures the impacts of both layers on future node interactions. We evaluate our proposed model on two downstream tasks of dynamic graph representation learning: dynamic link prediction and future node degree prediction. Compared to 12 state-of-the-art methods, our model consistently demonstrates superior performance, underscoring its effectiveness in capturing the complexities of graph evolution.