Learning on graphs, where instance nodes are inter-connected, has become one
of the central problems for deep learning, as relational structures are
pervasive and induce data inter-dependence which hinders trivial adaptation of
existing approaches that assume inputs to be i.i.d.~sampled. However, current
models mostly focus on improving testing performance of in-distribution data
and largely ignore the potential risk w.r.t. out-of-distribution (OOD) testing
samples that may cause negative outcome if the prediction is overconfident on
them. In this paper, we investigate the under-explored problem, OOD detection
on graph-structured data, and identify a provably effective OOD discriminator
based on an energy function directly extracted from graph neural networks
trained with standard classification loss. This paves a way for a simple,
powerful and efficient OOD detection model for GNN-based learning on graphs,
which we call GNNSafe. It also has nice theoretical properties that guarantee
an overall distinguishable margin between the detection scores for
in-distribution and OOD samples, which, more critically, can be further
strengthened by a learning-free energy belief propagation scheme. For
comprehensive evaluation, we introduce new benchmark settings that evaluate the
model for detecting OOD data from both synthetic and real distribution shifts
(cross-domain graph shifts and temporal graph shifts). The results show that
GNNSafe achieves up to $17.0\%$ AUROC improvement over state-of-the-arts and it
could serve as simple yet strong baselines in such an under-developed area.

针对图形数据的 OOD 检测问题，本论文提出了一种基于图神经网络的能量函数的有效 OOD 鉴别器，命名为 GNNSafe，并在模拟和真实数据集上的评估中证明了它的性能优于现有技术。