Papers › Dynamic Graph Representation Learning with Fourier Temporal State Embedding
Dynamic Graph Representation Learning with Fourier Temporal State Embedding
Yihan He, Wei Cao, Shun Zheng, Zhifeng Gao, Jiang Bian
Static graph representation learning has been applied in many tasks over the years thanks to the invention of unsupervised graph embedding methods and more recently, graph neural networks (GNNs). However, in many cases, we are to handle dynamic graphs where the structures of graphs and labels of the nodes are evolving steadily with time. This has posed a great challenge to existing methods in time and memory efficiency. In this work, we present a new method named Fourier Temporal State Embedding (FTSE) to address the temporal information in dynamic graph representation learning. FTSE offered time and memory-efficient solution through applying signal processing techniques to the temporal graph signals. We paired the Fourier Transform with an efficient edge network and provided a new prototype of modeling dynamic graph evolution with high precision. FTSE can also prevent the 'history explosion' that exists in sequential models. The empirical study shows that our proposed approach achieves significantly better performance than previous approaches on public datasets across multiple tasks.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections