In reliable decision-making systems based on machine learning, models have to be robust to distributional shifts or provide the uncertainty of their predictions. In node-level problems of graph learning, distributional shifts can be especially complex since the samples are interdependent. To evaluate the performance of graph models, it is important to test them on diverse and meaningful distributional shifts. However, most graph benchmarks considering distributional shifts for node-level problems focus mainly on node features, while structural properties are also essential for graph problems. In this work, we propose a general approach for inducing diverse distributional shifts based on graph structure. We use this approach to create data splits according to several structural node properties: popularity, locality, and density. In our experiments, we thoroughly evaluate the proposed distributional shifts and show that they can be quite challenging for existing graph models. We also reveal that simple models often outperform more sophisticated methods on the considered structural shifts. Finally, our experiments provide evidence that there is a trade-off between the quality of learned representations for the base classification task under structural distributional shift and the ability to separate the nodes from different distributions using these representations.

翻译：在基于机器学习的高可靠性决策系统中，模型需具备对分布偏移的鲁棒性，或能提供预测的不确定性。在图学习的节点级任务中，分布偏移可能尤为复杂，因为样本之间存在相互依赖关系。为评估图模型性能，需在多样化且具有实际意义的分布偏移条件下对其进行测试。然而，现有针对节点级任务分布偏移的图基准测试主要关注节点特征，而结构特性对图问题同样至关重要。本文提出一种基于图结构诱导多样化分布偏移的通用方法，并利用该方法根据多项结构节点属性（包括流行度、局部性和密度）构建数据划分。通过实验，我们全面评估了所提出的分布偏移，并表明其对现有图模型具有显著挑战性。同时发现，简单模型在处理此类结构性偏移时往往优于更复杂的模型。最终，实验证据表明：在结构性分布偏移下，基础分类任务所学表示的质量与利用这些表示区分不同分布节点的能力之间存在权衡关系。