Out-Of-Distribution Generalization on Graphs: A Survey
Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on the in-distribution hypothesis, i.e., testing and training graph data are identically distributed. However, this in-distribution hypothesis can hardly be satisfied in many real-world graph scenarios where the model performance substantially degrades when there exist distribution shifts between testing and training graph data. To solve this critical problem, out-of-distribution (OOD) generalization on graphs, which goes beyond the in-distribution hypothesis, has made great progress and attracted ever-increasing attention from the research community. In this paper, we comprehensively survey OOD generalization on graphs and present a detailed review of recent advances in this area. First, we provide a formal problem definition of OOD generalization on graphs. Second, we categorize existing methods into three classes from conceptually different perspectives, i.e., data, model, and learning strategy, based on their positions in the graph machine learning pipeline, followed by detailed discussions for each category. We also review the theories related to OOD generalization on graphs and introduce the commonly used graph datasets for thorough evaluations. Finally, we share our insights on future research directions. This paper is the first systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.
Code (1)
Tasks
Out-of-Distribution GeneralizationSurveySimilar Papers 제목 키워드 기반
Out-of-Distribution Generalization in Graph Foundation Models
Graphs are a fundamental data structure for representing relational information in domains such as social networks, molecular systems, and knowledge graphs. However, graph learning models often suffer from limited genera…
Knowledge GraphsGraph LearningBeyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs
Distribution shifts on graphs -- the data distribution discrepancies between training and testing a graph machine learning model, are often ubiquitous and unavoidable in real-world scenarios. Such shifts may severely det…
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
Distribution shifts on graphs -- the discrepancies in data distribution between training and employing a graph machine learning model -- are ubiquitous and often unavoidable in real-world scenarios. These shifts may seve…
Graph LearningOut-of-Distribution GeneralizationOut-of-Distribution Detection on Graphs: A Survey
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-w…
Anomaly DetectionGraph Anomaly DetectionOutlier DetectionOut-of-Distribution Detection+1A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs
Distribution shifts, in which the training distribution differs from the testing distribution, can significantly degrade the performance of Graph Neural Networks (GNNs). Although some existing graph classification benchm…
Domain GeneralizationGraph ClassificationOut-of-Distribution Generalization