Out-of-Distribution Detection on Graphs: A Survey

• URL: http://arxiv.org/abs/2502.08105v1
• Date: Wed, 12 Feb 2025 04:07:12 GMT
• Title: Out-of-Distribution Detection on Graphs: A Survey
• Authors: Tingyi Cai, Yunliang Jiang, Yixin Liu, Ming Li, Changqin Huang, Shirui Pan,
• Abstract summary: Graph out-of-distribution (GOOD) detection focuses on identifying graph data that deviates from the distribution seen during training. We categorize existing methods into four types: enhancement-based, reconstruction-based, information propagation-based, and classification-based approaches. We discuss practical applications and theoretical foundations, highlighting the unique challenges posed by graph data.
• Score: 58.47395497985277
• License:
• Abstract: Graph machine learning has witnessed rapid growth, driving advancements across diverse domains. However, the in-distribution assumption, where training and testing data share the same distribution, often breaks in real-world scenarios, leading to degraded model performance under distribution shifts. This challenge has catalyzed interest in graph out-of-distribution (GOOD) detection, which focuses on identifying graph data that deviates from the distribution seen during training, thereby enhancing model robustness. In this paper, we provide a rigorous definition of GOOD detection and systematically categorize existing methods into four types: enhancement-based, reconstruction-based, information propagation-based, and classification-based approaches. We analyze the principles and mechanisms of each approach and clarify the distinctions between GOOD detection and related fields, such as graph anomaly detection, outlier detection, and GOOD generalization. Beyond methodology, we discuss practical applications and theoretical foundations, highlighting the unique challenges posed by graph data. Finally, we discuss the primary challenges and propose future directions to advance this emerging field. The repository of this survey is available at https://github.com/ca1man-2022/Awesome-GOOD-Detection.

Related papers

• A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation [59.14165404728197]
  We provide an up-to-date and forward-looking review of deep graph learning under distribution shifts. Specifically, we cover three primary scenarios: graph OOD generalization, training-time graph OOD adaptation, and test-time graph OOD adaptation. To provide a better understanding of the literature, we systematically categorize the existing models based on our proposed taxonomy.
  arXiv  Detail & Related papers  (2024-10-25T02:39:56Z)
• GDDA: Semantic OOD Detection on Graphs under Covariate Shift via Score-Based Diffusion Models [8.562907330207716]
  Out-of-distribution (OOD) detection poses a significant challenge for Graph Neural Networks (GNNs) Most existing OOD detection methods on graphs primarily focus on identifying instances in test data domains. In this work, we address both types of shifts simultaneously and introduce a novel challenge for OOD detection on graphs.
  arXiv  Detail & Related papers  (2024-10-23T03:05:33Z)
• Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark [73.58840254552656]
  Unsupervised graph-level anomaly detection (GLAD) and unsupervised graph-level out-of-distribution (OOD) detection have received significant attention in recent years. We present a Unified Benchmark for unsupervised Graph-level OOD and anomaly Detection (our method) Our benchmark encompasses 35 datasets spanning four practical anomaly and OOD detection scenarios. We conduct multi-dimensional analyses to explore the effectiveness, generalizability, robustness, and efficiency of existing methods.
  arXiv  Detail & Related papers  (2024-06-21T04:07:43Z)
• Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs [22.561747395557642]
  We provide an up-to-date and forward-looking review of graph Out-Of-Distribution (OOD) adaptation methods. Based on our proposed taxonomy for graph OOD adaptation, we systematically categorize the existing methods according to their learning paradigm. We point out promising research directions and the corresponding challenges.
  arXiv  Detail & Related papers  (2024-02-17T00:40:12Z)
• DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization [58.704753031608625]
  Time series is one of the most challenging modalities in machine learning research. OOD detection and generalization on time series tend to suffer due to its non-stationary property. We propose DIVERSIFY, a framework for OOD detection and generalization on dynamic distributions of time series.
  arXiv  Detail & Related papers  (2023-08-04T12:27:11Z)
• 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)
• Handling Distribution Shifts on Graphs: An Invariance Perspective [78.31180235269035]
  We formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM) EERM resorts to multiple context explorers that are adversarially trained to maximize the variance of risks from multiple virtual environments. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution.
  arXiv  Detail & Related papers  (2022-02-05T02:31:01Z)
• Quantifying Challenges in the Application of Graph Representation Learning [0.0]
  We provide an application oriented perspective to a set of popular embedding approaches. We evaluate their representational power with respect to real-world graph properties. Our results suggest that "one-to-fit-all" GRL approaches are hard to define in real-world scenarios.
  arXiv  Detail & Related papers  (2020-06-18T03:19:43Z)

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.