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Energy-based Out-of-Distribution Detection for Graph Neural Networks
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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.
Forward citations
Cited by 13 Pith papers
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Hypergraph Neural Stochastic Diffusion: An SDE Framework for Uncertainty Estimation
HyperNSD models hypergraph node states as an incidence-aware SDE whose pathwise variability yields competitive uncertainty estimates for OOD and misclassification detection.
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Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs
LG-Plug mines pseudo-OOD exposures from clustered unlabeled nodes via iterative LLM prompting and regularizes topology-driven graph OOD detectors, cutting FPR95 by ≥7% across six TAG benchmarks.
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Subgraph Generation for Generalizing on Out-of-Distribution Links
FLEX is a generative framework that synthesizes counterfactual subgraphs with a semi-implicit graph VAE and adversarially co-trains a GNN to improve out-of-distribution link prediction.
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SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation
A training-free neighborhood score propagation method improves out-of-distribution detection for point clouds under synthetic-to-real domain shift.
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An Information-Theoretic Framework for Feature Construction in Out-of-Distribution Detection
A variational loss combining KL divergence and the Information Bottleneck predicts a piecewise-linear shaping function for OOD detection that beats existing element-wise shaping methods on ImageNet and CIFAR benchmarks.
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Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic Graphs
EviSEC combines evidential uncertainty and spectrum-aware contrastive training to detect out-of-distribution dynamic graphs, reporting strong AUROC gains over prior baselines.
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When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty
OGA combines prototype-based unknown-class rejection with LLM-generated, structure-guided annotations so text-attributed graphs can be retrained in open-world settings.
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Graph Synthetic Out-of-Distribution Exposure with Large Language Models
LLM-identified or LLM-generated pseudo-OOD nodes used as exposure data during GNN training improve node-level OOD detection on text-attributed graphs without real OOD labels.
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GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model
GLIP-OOD performs zero-shot node-level OOD detection on text-attributed graphs using a graph foundation model, optionally with LLM-generated pseudo-OOD labels, and matches or exceeds supervised baselines.
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Towards Precise Prediction Uncertainty in GNNs: Refining GNNs with Topology-grouping Strategy
A post-hoc GNN calibration method that groups nodes by confidence and neighborhood prediction similarity and applies group-specific temperatures, reducing expected calibration error on many benchmarks.
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Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs
NODESAFE adds two logit variance penalties to GNN training, cutting FPR95 for node-level OOD detection dramatically compared with GNNSAFE on citation and social-network graphs.
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SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps
SpecGap adjusts GNN features by subtracting the second-largest Laplacian eigenvector component times the spectral gap, claiming improved graph OOD detection without retraining.
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Handling Out-of-Distribution Data: A Survey
A survey that organizes covariate and semantic shift handling methods into one taxonomy and argues for unified models, while contributing no new experiments or benchmark evaluation.
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