Graph Neural Networks (GNNs) are state-of-the-art models for performing prediction tasks on graphs. While existing GNNs have shown great performance on various tasks related to graphs, little attention has been paid to the scenario where out-of-distribution (OOD) nodes exist in the graph during training and inference. Borrowing the concept from CV and NLP, we define OOD nodes as nodes with labels unseen from the training set. Since a lot of networks are automatically constructed by programs, real-world graphs are often noisy and may contain nodes from unknown distributions. In this work, we define the problem of graph learning with out-of-distribution nodes. Specifically, we aim to accomplish two tasks: 1) detect nodes which do not belong to the known distribution and 2) classify the remaining nodes to be one of the known classes. We demonstrate that the connection patterns in graphs are informative for outlier detection, and propose Out-of-Distribution Graph Attention Network (OODGAT), a novel GNN model which explicitly models the interaction between different kinds of nodes and separate inliers from outliers during feature propagation. Extensive experiments show that OODGAT outperforms existing outlier detection methods by a large margin, while being better or comparable in terms of in-distribution classification.

翻译：图神经网络（GNNs）是执行图上预测任务的最先进模型。尽管现有GNN在图相关任务中表现优异，但针对训练和推理过程中图中存在分布外（OOD）节点的场景却鲜有关注。借鉴计算机视觉和自然语言处理领域的概念，我们将OOD节点定义为标签未出现在训练集中的节点。由于许多网络由程序自动构建，真实世界中的图常存在噪声，可能包含来自未知分布的节点。本研究定义了含分布外节点的图学习问题，具体目标是完成两项任务：1）检测不属于已知分布的节点；2）将剩余节点分类至已知类别。实验表明，图中的连接模式对异常检测具有信息价值，为此我们提出分布外图注意力网络（OODGAT），这是一种新型GNN模型，通过显式建模不同类型节点间的交互，在特征传播过程中分离正常节点与异常节点。大量实验证明，OODGAT在异常检测任务上显著优于现有方法，同时在分布内分类任务中表现相当或更优。