Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs
ICLR 2024(2024)
摘要
We present the Evolving Graph Fourier Transform (EFT), the first invertible
spectral transform that captures evolving representations on temporal graphs.
We motivate our work by the inadequacy of existing methods for capturing the
evolving graph spectra, which are also computationally expensive due to the
temporal aspect along with the graph vertex domain. We view the problem as an
optimization over the Laplacian of the continuous time dynamic graph.
Additionally, we propose pseudo-spectrum relaxations that decompose the
transformation process, making it highly computationally efficient. The EFT
method adeptly captures the evolving graph's structural and positional
properties, making it effective for downstream tasks on evolving graphs. Hence,
as a reference implementation, we develop a simple neural model induced with
EFT for capturing evolving graph spectra. We empirically validate our
theoretical findings on a number of large-scale and standard temporal graph
benchmarks and demonstrate that our model achieves state-of-the-art
performance.
更多查看译文
关键词
Temporal Dynamic Graphs,Spectral Transform,GNN
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要