通过时空感知的随机游走，自动识别并弱化动态图中的噪声边。
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
- 基于图小波系数分析节点时空位置与边演变趋势，定位噪声源。
- 在多个数据集上显著提升节点分类与链接预测准确率。
- 适合处理含噪声的动态图学习任务，如社交网络演化建模。
动态图增强被用于提升动态图神经网络的性能。现有方法通常假设时间局部性，即近期边比早期边更关键。然而，对于由随机噪声引起的边变化，过度强调近期边而忽略早期边可能导致模型学习到噪声。为此，我们提出STAA（时空活跃感知随机游走扩散）：通过图小波系数分析节点的空间关键位置，以及通过图小波系数变化率分析边的时间演变特征，识别可能包含噪声边的节点。随后，利用随机游走降低噪声边权重，构建融合时空信息的扩散矩阵，作为动态图神经网络的学习增广邻接矩阵。在多个数据集上的实验表明，STAA在节点分类和链接预测任务中均优于其他动态图增强方法。
原文摘要 · Abstract (English)
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.
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