SGD-DyG: Self-Reliant Global Dependency Apprehending on Dynamic Graphs

Dynamic graphs offer more precise modeling of real-world applications compared to static graphs. Therefore, learning on dynamic graphs has garnered significant research attention in recent years. Unfortunately, the current approaches for learning dynamic graphs remain inadequate in capturing global spatial-temporal dependency. This issue arises from capturing biased spatial and temporal dependencies, thereby weakening the coupling between spatial and temporal dimensions. To overcome it, we propose a Self-Reliant Global Dependency Apprehending Framework on Dynamic Graphs, namely SGD-DyG. Specifically, we first design a frequency self-enhanced learning module that examines the global inherent interactions of the node features hidden in the frequency domain. Furthermore, we propose a global-local-mixed self-supervised learning module that maximizes spatial-temporal mutual information between local node and global graph embeddings. Extensive experiments on seven real-world dynamic graph datasets validate that the proposed SGD-DyG consistently exceeds state-of-the-art models.