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 that consider distributional shifts for node-level problems focus mainly on node features, while data in graph problems is primarily defined by its structural properties. 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 are quite challenging for existing graph models. We hope that the proposed approach will be helpful for the further development of reliable graph machine learning.
翻译:在基于机器学习的可靠决策系统中,模型必须稳健地适应分布式转移,或提供预测的不确定性。在图表学习的节点问题中,分布式转移可能特别复杂,因为样本是相互依存的。为了评估图形模型的性能,重要的是要用多样和有意义的分布式转移来测试这些模型。然而,大多数考虑节点问题分布式转移的图表基准主要侧重于节点特征,而图表中的数据问题主要由其结构属性来界定。在这项工作中,我们提出一种引导基于图形结构的不同分布式转移的一般方法。我们使用这种方法根据几个结构节点(普及性、地点性和密度)来创建数据分裂。我们在实验中,彻底评估拟议的分布式转移,并表明它们对于现有的图表模型来说相当具有挑战性。我们希望,拟议的方法将有助于进一步发展可靠的图形机学习。</s>