Out-of-Distribution Detection on Graphs: A Survey

2025-02-12

AI Summary

Graph machine learning often suffers from distributional shift between training and test data in real-world scenarios, undermining model robustness. This paper systematically investigates the Graph Out-of-Distribution (GOOD) detection problem—identifying whether test graphs deviate from the training distribution. We formally define GOOD detection for the first time and propose a unified taxonomy encompassing four methodological categories: augmentation-based, reconstruction-based, message-passing-based, and classification-based approaches. Crucially, we rigorously distinguish GOOD detection from related tasks—including graph anomaly detection, outlier detection, and OOD generalization—highlighting its unique objectives and assumptions. Furthermore, we establish the first comprehensive theoretical framework and methodological spectrum for GOOD detection, and publicly release Awesome-GOOD-Detection, an authoritative open-source repository. This resource provides standardized benchmarks, reproducible evaluation tools, and a roadmap for future research, thereby laying foundational infrastructure for the emerging field of GOOD detection.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Reasoning under Uncertainty: Graphical Models

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
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Detects graph data deviating from training distribution
• Enhances model robustness under distribution shifts
• Categorizes detection methods into four types

Innovation

• Graph out-of-distribution detection
• Four methodological categories
• Enhancing model robustness