embedding-predictive-architecture

Proposes SG-JEPA, a joint spiking embedding predictive architecture for large-scale dynamic graphs that partitions nodes into context and target sets along the temporal dimension to learn predictive embeddings, achieving competitive performance on node classification while scaling to graphs with 13 million edges and avoiding complex self-supervised mechanisms.