Abstract
Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by noisy and redundant graph data. As a prominent solution, Graph Augmentation Learning (GAL) has recently received increasing attention. Among prior GAL approaches, edge-dropping methods that randomly remove edges from a graph during training are effective techniques to improve the robustness of GNNs. However, randomly dropping edges often results in bypassing critical edges, consequently weakening the effectiveness of message passing. In this paper, we propose a novel adversarial edge-dropping method (ADEdgeDrop) that leverages an adversarial edge predictor guiding the removal of edges, which can be flexibly incorporated into diverse GNN backbones. Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method. The proposed ADEdgeDrop is optimized alternately by stochastic gradient descent and projected gradient descent. Comprehensive experiments on six graph benchmark datasets demonstrate that the proposed ADEdgeDrop outperforms state-of-the-art baselines across various GNN backbones, demonstrating improved generalization and robustness.
Abstract (translated)
尽管图神经网络（GNNs）通过各种消息传递机制表现出从邻居节点收集图形结构信息的力量，但GNNs的性能受到噪声和冗余图形数据导致的泛化能力和脆弱性的限制。作为突出的解决方案，Graph Augmentation Learning (GAL) 最近受到了越来越多的关注。在先前的GAL方法中，训练期间随机从图中删除边是一种有效的改进GNNs robust性的技术。然而，随机删除边通常会导致绕过关键边，从而削弱了消息传递的有效性。在本文中，我们提出了一种新颖的 adversarial edge-dropping 方法（ADEdgeDrop），它利用 adversarial edge predictor 指导删除边，可以灵活地集成到各种 GNN 骨干网络中。采用 adversarial 训练框架，adversarial edge predictor 使用从原始图中转换的线图估计要删除的边，这改善了边缘删除方法的解释性。所提出的 ADEdgeDrop 通过交替使用随机梯度下降和投影梯度下降进行优化。对六个图形基准数据集的全面实验证明，与最先进的基线相比，所提出的 ADEdgeDrop 在各种 GNN 骨干网络上都表现出卓越的性能，证明了改进的泛化能力和脆弱性。
URL
https://arxiv.org/abs/2403.09171
