Graph neural networks (GNNs) have excelled in various graph learning tasks, particularly node classification. However, their performance is often hampered by noisy measurements in real-world graphs, which can corrupt critical patterns in the data. To address this, we propose a novel uncertainty-aware graph learning framework inspired by distributionally robust optimization. Specifically, we use a graph neural network-based encoder to embed the node features and find the optimal node embeddings by minimizing the worst-case risk through a minimax formulation. Such an uncertainty-aware learning process leads to improved node representations and a more robust graph predictive model that effectively mitigates the impact of uncertainty arising from data noise. Our experimental results demonstrate superior predictive performance over baselines across noisy scenarios.

翻译：图神经网络（GNNs）在各种图学习任务，尤其是节点分类中表现出色。然而，其性能常受现实世界图中噪声测量的影响，这些噪声可能破坏数据中的关键模式。为解决此问题，我们提出一种受分布鲁棒优化启发的新型不确定性感知图学习框架。具体而言，我们采用基于图神经网络的编码器嵌入节点特征，并通过极小极大化公式最小化最坏情况风险，从而找到最优节点嵌入。这种不确定性感知的学习过程能产生改进的节点表示和更鲁棒的图预测模型，有效缓解由数据噪声引起的不确定性的影响。我们的实验结果表明，在多种噪声场景下，该模型均优于基线方法，展现出卓越的预测性能。