论文标题
处理分配变化图：不变性透视图
Handling Distribution Shifts on Graphs: An Invariance Perspective
论文作者
论文摘要
越来越多的证据表明神经网络对分布变化的敏感性，因此对分布（OOD）概括的研究引起了人们的关注。 Nonetheless, current endeavors mostly focus on Euclidean data, and its formulation for graph-structured data is not clear and remains under-explored, given two-fold fundamental challenges: 1) the inter-connection among nodes in one graph, which induces non-IID generation of data points even under the same environment, and 2) the structural information in the input graph, which is also informative for prediction.在本文中，我们在图表上制定了OOD问题，并开发了一种新的不变学习方法，探索到驱除风险最小化（EERM），从而促进了图神经网络以利用预测的不变性原则。 EERM求助于多个上下文资源管理器（在我们案例中指定为图形结构编辑器），这些探索器受对抗训练，以最大程度地提高来自多个虚拟环境的风险差异。这样的设计使该模型可以从单个观察到的环境中推断出，这是节点级预测的常见情况。我们通过从理论上显示其对有效的OOD解决方案的保证来证明我们方法的有效性，并进一步证明了其在各种现实世界数据集上的功能，以处理分布从人工伪造特征，跨域转移和动态图演变的转移。
There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so that research on out-of-distribution (OOD) generalization comes into the spotlight. Nonetheless, current endeavors mostly focus on Euclidean data, and its formulation for graph-structured data is not clear and remains under-explored, given two-fold fundamental challenges: 1) the inter-connection among nodes in one graph, which induces non-IID generation of data points even under the same environment, and 2) the structural information in the input graph, which is also informative for prediction. In this paper, we formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM), that facilitates graph neural networks to leverage invariance principles for prediction. EERM resorts to multiple context explorers (specified as graph structure editers in our case) that are adversarially trained to maximize the variance of risks from multiple virtual environments. Such a design enables the model to extrapolate from a single observed environment which is the common case for node-level prediction. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution and further demonstrate its power on various real-world datasets for handling distribution shifts from artificial spurious features, cross-domain transfers and dynamic graph evolution.
