Energy-based Out-of-Distribution Detection for Graph Neural Networks
- URL: http://arxiv.org/abs/2302.02914v1
- Date: Mon, 6 Feb 2023 16:38:43 GMT
- Title: Energy-based Out-of-Distribution Detection for Graph Neural Networks
- Authors: Qitian Wu, Yiting Chen, Chenxiao Yang, Junchi Yan
- Abstract summary: We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe.
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.
- Score: 76.0242218180483
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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