Graph Out-of-Distribution Detection via Test-Time Calibration with Dual Dynamic Dictionaries

• URL: http://arxiv.org/abs/2511.13541v2
• Date: Sun, 23 Nov 2025 07:09:35 GMT
• Title: Graph Out-of-Distribution Detection via Test-Time Calibration with Dual Dynamic Dictionaries
• Authors: Yue Hou, Ruomei Liu, Yingke Su, Junran Wu, Ke Xu,
• Abstract summary: Key challenge in graph out-of-distribution (OOD) detection lies in the absence of ground-truth OOD samples during training.<n>We propose a novel test-time graph OOD detection method, termed BaCa, that calibrates OOD scores using dual dynamically updated dictionaries.<n>BaCa significantly outperforms existing state-of-the-art methods in OOD detection.
• Score: 15.43092254877282
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A key challenge in graph out-of-distribution (OOD) detection lies in the absence of ground-truth OOD samples during training. Existing methods are typically optimized to capture features within the in-distribution (ID) data and calculate OOD scores, which often limits pre-trained models from representing distributional boundaries, leading to unreliable OOD detection. Moreover, the latent structure of graph data is often governed by multiple underlying factors, which remains less explored. To address these challenges, we propose a novel test-time graph OOD detection method, termed BaCa, that calibrates OOD scores using dual dynamically updated dictionaries without requiring fine-tuning the pre-trained model. Specifically, BaCa estimates graphons and applies a mix-up strategy solely with test samples to generate diverse boundary-aware discriminative topologies, eliminating the need for exposing auxiliary datasets as outliers. We construct dual dynamic dictionaries via priority queues and attention mechanisms to adaptively capture latent ID and OOD representations, which are then utilized for boundary-aware OOD score calibration. To the best of our knowledge, extensive experiments on real-world datasets show that BaCa significantly outperforms existing state-of-the-art methods in OOD detection.

Related papers

• Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection [51.93878677594561]
  In unsupervised graph-level OOD detection, models are typically trained using only in-distribution (ID) data.<n>We propose a Policy-Guided Outlier Synthesis framework that replaces statics with a learned exploration strategy.
  arXiv  Detail & Related papers  (2026-02-28T11:40:18Z)
• Beyond Binary Out-of-Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectories [6.634696833862699]
  We introduce DISC: Diffusion-based Statistical characterization.<n> DISC captures statistical discrepancies across multiple noise levels.<n>It matches or surpasses state-of-the-art detectors for OOD detection and, crucially, also classifies OOD type.
  arXiv  Detail & Related papers  (2025-10-20T10:18:45Z)
• Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic Graphs [17.750640850821622]
  Out-of-distribution (OOD) detection in dynamic graphs aims to identify whether incoming data deviates from the distribution of the in-distribution (ID) training set.<n>We propose EviSEC, an innovative and effective OOD detector via Evidential Spectrum-awarE Contrastive Learning.
  arXiv  Detail & Related papers  (2025-06-09T04:34:46Z)
• Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes [13.246251147975192]
  Deep neural networks (DNNs) are often constructed under the closed-world assumption.<n>OOD samples are not always available during the training phase in real-world applications.<n>We propose a Gaussian-process-based OOD detection method to establish a decision boundary based on InD data only.
  arXiv  Detail & Related papers  (2024-12-30T12:57:31Z)
• Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models [71.39421638547164]
  We propose to detect OOD molecules by adopting an auxiliary diffusion model-based framework, which compares similarities between input molecules and reconstructed graphs. Due to the generative bias towards reconstructing ID training samples, the similarity scores of OOD molecules will be much lower to facilitate detection. Our research pioneers an approach of Prototypical Graph Reconstruction for Molecular OOD Detection, dubbed as PGR-MOOD and hinges on three innovations.
  arXiv  Detail & Related papers  (2024-04-24T03:25:53Z)
• EAT: Towards Long-Tailed Out-of-Distribution Detection [55.380390767978554]
  This paper addresses the challenging task of long-tailed OOD detection. The main difficulty lies in distinguishing OOD data from samples belonging to the tail classes. We propose two simple ideas: (1) Expanding the in-distribution class space by introducing multiple abstention classes, and (2) Augmenting the context-limited tail classes by overlaying images onto the context-rich OOD data.
  arXiv  Detail & Related papers  (2023-12-14T13:47:13Z)
• Model-free Test Time Adaptation for Out-Of-Distribution Detection [62.49795078366206]
  We propose a Non-Parametric Test Time textbfAdaptation framework for textbfDistribution textbfDetection (abbr) abbr utilizes online test samples for model adaptation during testing, enhancing adaptability to changing data distributions. We demonstrate the effectiveness of abbr through comprehensive experiments on multiple OOD detection benchmarks.
  arXiv  Detail & Related papers  (2023-11-28T02:00:47Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  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.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection [67.90365841083951]
  We develop a new graph contrastive learning framework GOOD-D for detecting OOD graphs without using any ground-truth labels. GOOD-D is able to capture the latent ID patterns and accurately detect OOD graphs based on the semantic inconsistency in different granularities. As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-11-08T12:41:58Z)
• Pseudo-OOD training for robust language models [78.15712542481859]
  OOD detection is a key component of a reliable machine-learning model for any industry-scale application. We propose POORE - POsthoc pseudo-Ood REgularization, that generates pseudo-OOD samples using in-distribution (IND) data. We extensively evaluate our framework on three real-world dialogue systems, achieving new state-of-the-art in OOD detection.
  arXiv  Detail & Related papers  (2022-10-17T14:32:02Z)
• A Simple Test-Time Method for Out-of-Distribution Detection [45.11199798139358]
  This paper proposes a simple Test-time Linear Training (ETLT) method for OOD detection. We find that the probabilities of input images being out-of-distribution are surprisingly linearly correlated to the features extracted by neural networks. We propose an online variant of the proposed method, which achieves promising performance and is more practical in real-world applications.
  arXiv  Detail & Related papers  (2022-07-17T16:02:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.