SiGNN, a novel framework, effectively integrates the temporal dynamics of Spiking Neural Networks (SNNs) with the powerful capabilities of Graph Neural Networks (GNNs) to learn enhanced spatial-temporal node representations on dynamic graphs.
A novel recurrent learning framework named Recurrent Structure-reinforced Graph Transformer (RSGT) that explicitly models the temporal states of edges in dynamic graphs to enhance the learning of node representations.