Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning

TL;DR

This paper introduces STAA, a novel spatiotemporal augmentation method for dynamic GNNs that identifies and reduces noisy edges using wavelet analysis, improving performance on node classification and link prediction.

Contribution

STAA is the first method to analyze and mitigate noisy edges in dynamic graphs through spatiotemporal wavelet coefficients, enhancing GNN learning.

Findings

STAA outperforms existing augmentation methods in node classification.

STAA improves link prediction accuracy.

Wavelet-based analysis effectively identifies noisy edges.

Abstract

Dynamic graph augmentation is used to improve the performance of dynamic GNNs. Most methods assume temporal locality, meaning that recent edges are more influential than earlier edges. However, for temporal changes in edges caused by random noise, overemphasizing recent edges while neglecting earlier ones may lead to the model capturing noise. To address this issue, we propose STAA (SpatioTemporal Activity-Aware Random Walk Diffusion). STAA identifies nodes likely to have noisy edges in spatiotemporal dimensions. Spatially, it analyzes critical topological positions through graph wavelet coefficients. Temporally, it analyzes edge evolution through graph wavelet coefficient change rates. Then, random walks are used to reduce the weights of noisy edges, deriving a diffusion matrix containing spatiotemporal information as an augmented adjacency matrix for dynamic GNN learning. Experiments on multiple datasets show that STAA outperforms other dynamic graph augmentation methods in node classification and link prediction tasks.