Renjie Gong

DeepSTE: Deep Spectral Temporal Embeddings for Dynamic Graph Representation Learning

Temporal embeddings play a crucial role in dynamic graph neural networks (DGNNs) by capturing the temporal dynamics of interactions. However, existing Random Fourier Feature (RFF)-based methods in DGNNs directly sample Fourier frequencies from a fixed, data-independent distribution $p(\omega)$, negl