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.

本研究解决了动态图神经网络在动态边缘学习中因过度强调最近边缘而导致捕捉噪声的问题。提出的STAA方法通过时空活动感知随机游走扩散来识别潜在的噪声边缘，利用图小波系数分析关键拓扑位置和边缘演化，从而有效减少噪声边缘的权重，实验结果表明STAA在节点分类和链接预测任务上优于其他动态图增强方法。

提高动态图学习的自适应时空增强