Conference
Open access
Sep 2026
DeepSTE: Deep Spectral Temporal Embeddings for Dynamic Graph Representation Learning
DeepSTE is proposed, a deep spectral temporal embedding framework for dynamic graphs that learns RFF representations via Monte Carlo importance sampling with a tractable proposal distribution and adopts a data-dependent scale parameter to construct the frequency proposal distribution reflecting time–frequency uncertain...
Qiang Huang, Ke Liu, Ren-Jie Gong et al.
· Proceedings of the Thirty-Fi... · 0 citations