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.

翻译：尽管图神经网络（GNNs）已通过多种消息传递机制展现了从邻域节点收集图结构信息的强大能力，但其性能因噪声和冗余图数据导致的泛化能力差与鲁棒性脆弱而受到限制。作为重要解决方案，图增强学习（GAL）近年来受到日益关注。在已有的GAL方法中，训练过程中随机移除图中边的边丢弃技术是提升GNNs鲁棒性的有效手段。然而，随机丢弃边常导致关键边被绕过，从而削弱消息传递的有效性。本文提出一种新型对抗性边丢弃方法（ADEdgeDrop），该方法利用对抗性边预测器指导边的移除，可灵活集成至多种GNN骨干网络中。通过采用对抗训练框架，该边预测器利用原始图转化的线图估计待丢弃的边，从而提高边丢弃方法的可解释性。所提出的ADEdgeDrop通过随机梯度下降与投影梯度下降交替优化。在六个图基准数据集上的综合实验表明，所提出的ADEdgeDrop在多种GNN骨干网络上均优于当前最先进的基线方法，展现出更强的泛化能力与鲁棒性。