A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation

• URL: http://arxiv.org/abs/2410.19265v1
• Date: Fri, 25 Oct 2024 02:39:56 GMT
• Title: A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
• Authors: Kexin Zhang, Shuhan Liu, Song Wang, Weili Shi, Chen Chen, Pan Li, Sheng Li, Jundong Li, Kaize Ding,
• Abstract summary: 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.
• Score: 59.14165404728197
• License:
• Abstract: 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 severely deteriorate model performance, posing significant challenges for reliable graph machine learning. Consequently, there has been a surge in research on graph machine learning under distribution shifts, aiming to train models to achieve satisfactory performance on out-of-distribution (OOD) test data. In our survey, 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. We begin by formally formulating the problems and discussing various types of distribution shifts that can affect graph learning, such as covariate shifts and concept shifts. To provide a better understanding of the literature, we systematically categorize the existing models based on our proposed taxonomy and investigate the adopted techniques behind. We also summarize commonly used datasets in this research area to facilitate further investigation. Finally, we point out promising research directions and the corresponding challenges to encourage further study in this vital domain. Additionally, we provide a continuously updated reading list at https://github.com/kaize0409/Awesome-Graph-OOD.

Related papers

• Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning [53.81365215811222]
  We provide a review and summary of the latest approaches, strategies, and insights that address distribution shifts within the context of graph learning. We categorize existing graph learning methods into several essential scenarios, including graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning. We discuss the potential applications and future directions for graph learning under distribution shifts with a systematic analysis of the current state in this field.
  arXiv  Detail & Related papers  (2024-02-26T07:52:40Z)
• 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)
• GOODAT: Towards Test-time Graph Out-of-Distribution Detection [103.40396427724667]
  Graph neural networks (GNNs) have found widespread application in modeling graph data across diverse domains. Recent studies have explored graph OOD detection, often focusing on training a specific model or modifying the data on top of a well-trained GNN. This paper introduces a data-centric, unsupervised, and plug-and-play solution that operates independently of training data and modifications of GNN architecture.
  arXiv  Detail & Related papers  (2024-01-10T08:37:39Z)
• Graph Out-of-Distribution Generalization with Controllable Data Augmentation [51.17476258673232]
  Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties. Due to the selection bias of training and testing data, distribution deviation is widespread. We propose OOD calibration to measure the distribution deviation of virtual samples.
  arXiv  Detail & Related papers  (2023-08-16T13:10:27Z)
• Addressing the Impact of Localized Training Data in Graph Neural Networks [0.0]
  Graph Neural Networks (GNNs) have achieved notable success in learning from graph-structured data. This article aims to assess the impact of training GNNs on localized subsets of the graph. We propose a regularization method to minimize distributional discrepancies between localized training data and graph inference.
  arXiv  Detail & Related papers  (2023-07-24T11:04:22Z)
• Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization [54.64375566326931]
  Out-of-distribution (OOD) generalization deals with the prevalent learning scenario where test distribution shifts from training distribution. We propose to achieve graph OOD generalization with the novel design of non-Euclidean-space linear extrapolation. Our design tailors OOD samples for specific shifts without corrupting underlying causal mechanisms.
  arXiv  Detail & Related papers  (2023-06-13T18:46:28Z)
• Counterfactual Learning on Graphs: A Survey [34.47646823407408]
  Graph neural networks (GNNs) have achieved great success in representation learning on graphs. Counterfactual learning on graphs has shown promising results in alleviating these drawbacks. Various approaches have been proposed for counterfactual fairness, explainability, link prediction and other applications on graphs.
  arXiv  Detail & Related papers  (2023-04-03T21:42:42Z)
• OOD-GNN: Out-of-Distribution Generalized Graph Neural Network [73.67049248445277]
  Graph neural networks (GNNs) have achieved impressive performance when testing and training graph data come from identical distribution. Existing GNNs lack out-of-distribution generalization abilities so that their performance substantially degrades when there exist distribution shifts between testing and training graph data. We propose an out-of-distribution generalized graph neural network (OOD-GNN) for achieving satisfactory performance on unseen testing graphs that have different distributions with training graphs.
  arXiv  Detail & Related papers  (2021-12-07T16:29:10Z)

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.