  Dynamic graphs are ubiquitous in the real world, yet there is a lack of
suitable theoretical frameworks to effectively extend existing static graph
models into the temporal domain. Additionally, for link prediction tasks on
discrete dynamic graphs, the requirement of substantial GPU memory to store
embeddings of all nodes hinders the scalability of existing models. In this
paper, we introduce an Input {\bf S}napshots {\bf F}usion based {\bf Dy}namic
{\bf G}raph Neural Network (SFDyG). By eliminating the partitioning of
snapshots within the input window, we obtain a multi-graph (more than one edge
between two nodes). Subsequently, by introducing a graph denoising problem with
the assumption of temporal decayed smoothing, we integrate Hawkes process
theory into Graph Neural Networks to model the generated multi-graph.
Furthermore, based on the multi-graph, we propose a scalable three-step
mini-batch training method and demonstrate its equivalence to full-batch
training counterpart. Our experiments, conducted on eight distinct dynamic
graph datasets for future link prediction tasks, revealed that SFDyG generally
surpasses related methods.
