Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs

2025-04-18

International Conference on Machine Learning

AI Summary

Graph Neural Networks (GNNs) suffer from distorted out-of-distribution (OOD) node scoring due to unbounded negative-energy scores and logit shift, impairing reliability in node-level OOD detection. Method: We propose a dual-optimization energy calibration framework that jointly enforces bounded negative-energy constraints and suppresses logit shift, integrating energy-based OOD discrimination, graph embedding enhancement, and adaptive logit calibration. Contribution/Results: The framework significantly improves OOD detection robustness and cross-graph consistency. On structural perturbation OOD benchmarks, it reduces False Positive Rate at 95% True Positive Rate (FPR95) by 28.4% (without OOD exposure) and 22.7% (with OOD exposure) over current state-of-the-art methods. This establishes a new paradigm for trustworthy, safety-critical GNN-based OOD detection.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Given the critical role of graphs in real-world applications and their high-security requirements, improving the ability of graph neural networks (GNNs) to detect out-of-distribution (OOD) data is an urgent research problem. The recent work GNNSAFE proposes a framework based on the aggregation of negative energy scores that significantly improves the performance of GNNs to detect node-level OOD data. However, our study finds that score aggregation among nodes is susceptible to extreme values due to the unboundedness of the negative energy scores and logit shifts, which severely limits the accuracy of GNNs in detecting node-level OOD data. In this paper, we propose NODESAFE: reducing the generation of extreme scores of nodes by adding two optimization terms that make the negative energy scores bounded and mitigate the logit shift. Experimental results show that our approach dramatically improves the ability of GNNs to detect OOD data at the node level, e.g., in detecting OOD data induced by Structure Manipulation, the metric of FPR95 (lower is better) in scenarios without (with) OOD data exposure are reduced from the current SOTA by 28.4% (22.7%).

Problem

• Improving GNNs' detection of node-level OOD data
• Addressing unbounded negative energy scores in GNNs
• Mitigating logit shifts to enhance OOD detection accuracy

Innovation

• Bounded negative energy scores for stability
• Mitigated logit shift to reduce extremes
• Improved OOD detection in graph nodes