Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift

• URL: http://arxiv.org/abs/2211.02843v2
• Date: Thu, 21 Dec 2023 06:43:36 GMT
• Title: Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
• Authors: Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang, Xiangnan He
• Abstract summary: We propose a simple-yet-effective data augmentation strategy, Adversarial Invariant Augmentation (AIA) AIA aims to extrapolate and generate new environments, while concurrently preserving the original stable features during the augmentation process.
• Score: 50.98086766507025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The issue of distribution shifts is emerging as a critical concern in graph representation learning. From the perspective of invariant learning and stable learning, a recently well-established paradigm for out-of-distribution generalization, stable features of the graph are assumed to causally determine labels, while environmental features tend to be unstable and can lead to the two primary types of distribution shifts. The correlation shift is often caused by the spurious correlation between environmental features and labels that differs between the training and test data; the covariate shift often stems from the presence of new environmental features in test data. However, most strategies, such as invariant learning or graph augmentation, typically struggle with limited training environments or perturbed stable features, thus exposing limitations in handling the problem of covariate shift. To address this challenge, we propose a simple-yet-effective data augmentation strategy, Adversarial Invariant Augmentation (AIA), to handle the covariate shift on graphs. Specifically, given the training data, AIA aims to extrapolate and generate new environments, while concurrently preserving the original stable features during the augmentation process. Such a design equips the graph classification model with an enhanced capability to identify stable features in new environments, thereby effectively tackling the covariate shift in data. Extensive experiments with in-depth empirical analysis demonstrate the superiority of our approach. The implementation codes are publicly available at https://github.com/yongduosui/AIA.

Related papers

• Mitigating Graph Covariate Shift via Score-based Out-of-distribution Augmentation [16.59129444793973]
  Distribution shifts between training and testing datasets significantly impair the model performance on graph learning. We introduce a novel approach using score-based graph generation strategies that synthesize unseen environmental features while preserving the validity and stable features of overall graph patterns.
  arXiv  Detail & Related papers  (2024-10-23T02:09:02Z)
• Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization [9.116601683256317]
  In this work, we propose a novel framework, called Invariant Graph Learning based on Information bottleneck theory (InfoIGL) Specifically, InfoIGL introduces a redundancy filter to compress task-irrelevant information related to environmental factors. Experiments on both synthetic and real-world datasets demonstrate that our method achieves state-of-the-art performance under OOD generalization.
  arXiv  Detail & Related papers  (2024-08-03T07:38:04Z)
• Empowering Graph Invariance Learning with Deep Spurious Infomax [27.53568333416706]
  We introduce a novel graph invariance learning paradigm, which induces a robust and general inductive bias. EQuAD shows stable and enhanced performance across different degrees of bias in synthetic datasets and challenging real-world datasets up to $31.76%$.
  arXiv  Detail & Related papers  (2024-07-13T14:18:47Z)
• Graph Out-of-Distribution Generalization via Causal Intervention [69.70137479660113]
  We introduce a conceptually simple yet principled approach for training robust graph neural networks (GNNs) under node-level distribution shifts. Our method resorts to a new learning objective derived from causal inference that coordinates an environment estimator and a mixture-of-expert GNN predictor. Our model can effectively enhance generalization with various types of distribution shifts and yield up to 27.4% accuracy improvement over state-of-the-arts on graph OOD generalization benchmarks.
  arXiv  Detail & Related papers  (2024-02-18T07:49:22Z)
• Does Invariant Graph Learning via Environment Augmentation Learn Invariance? [39.08988313527199]
  Invariant graph representation learning aims to learn the invariance among data from different environments for out-of-distribution generalization on graphs. We develop a set of minimal assumptions, including variation sufficiency and variation consistency, for feasible invariant graph learning. We show that extracting the maximally invariant subgraph to the proxy predictions provably identifies the underlying invariant subgraph for successful OOD generalization.
  arXiv  Detail & Related papers  (2023-10-29T14:57:37Z)
• 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)
• 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)
• Stable Prediction on Graphs with Agnostic Distribution Shift [105.12836224149633]
  Graph neural networks (GNNs) have been shown to be effective on various graph tasks with randomly separated training and testing data. In real applications, however, the distribution of training graph might be different from that of the test one. We propose a novel stable prediction framework for GNNs, which permits both locally and globally stable learning and prediction on graphs.
  arXiv  Detail & Related papers  (2021-10-08T02:45:47Z)
• Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift [81.74795324629712]
  We call prediction-time batch normalization, which significantly improves model accuracy and calibration under covariate shift. We show that prediction-time batch normalization provides complementary benefits to existing state-of-the-art approaches for improving robustness. The method has mixed results when used alongside pre-training, and does not seem to perform as well under more natural types of dataset shift.
  arXiv  Detail & Related papers  (2020-06-19T05:08:43Z)

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.