Improving out-of-distribution generalization in graphs via hierarchical semantic environments

• URL: http://arxiv.org/abs/2403.01773v2
• Date: Mon, 3 Jun 2024 05:05:24 GMT
• Title: Improving out-of-distribution generalization in graphs via hierarchical semantic environments
• Authors: Yinhua Piao, Sangseon Lee, Yijingxiu Lu, Sun Kim,
• Abstract summary: We propose a novel approach to generate hierarchical environments for each graph. We introduce a new learning objective that guides our model to learn the diversity of environments within the same hierarchy. Our framework achieves up to 1.29% and 2.83% improvement over the best baselines on IC50 and EC50 prediction tasks, respectively.
• Score: 5.481047026874547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Out-of-distribution (OOD) generalization in the graph domain is challenging due to complex distribution shifts and a lack of environmental contexts. Recent methods attempt to enhance graph OOD generalization by generating flat environments. However, such flat environments come with inherent limitations to capture more complex data distributions. Considering the DrugOOD dataset, which contains diverse training environments (e.g., scaffold, size, etc.), flat contexts cannot sufficiently address its high heterogeneity. Thus, a new challenge is posed to generate more semantically enriched environments to enhance graph invariant learning for handling distribution shifts. In this paper, we propose a novel approach to generate hierarchical semantic environments for each graph. Firstly, given an input graph, we explicitly extract variant subgraphs from the input graph to generate proxy predictions on local environments. Then, stochastic attention mechanisms are employed to re-extract the subgraphs for regenerating global environments in a hierarchical manner. In addition, we introduce a new learning objective that guides our model to learn the diversity of environments within the same hierarchy while maintaining consistency across different hierarchies. This approach enables our model to consider the relationships between environments and facilitates robust graph invariant learning. Extensive experiments on real-world graph data have demonstrated the effectiveness of our framework. Particularly, in the challenging dataset DrugOOD, our method achieves up to 1.29% and 2.83% improvement over the best baselines on IC50 and EC50 prediction tasks, respectively.

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)
• Improving Graph Out-of-distribution Generalization on Real-world Data [25.328653597674197]
  This paper presents the theorems of environment-label dependency and mutable rationale invariance. Based on analytic investigations, a novel variational inference based method named Probability Dependency on Environments and Rationales for OOD Graphs on Real-world Data'' is introduced.
  arXiv  Detail & Related papers  (2024-07-14T13:48:25Z)
• IENE: Identifying and Extrapolating the Node Environment for Out-of-Distribution Generalization on Graphs [10.087216264788097]
  We propose IENE, an OOD generalization method on graphs based on node-level environmental identification and extrapolation techniques. It strengthens the model's ability to extract invariance from two granularities simultaneously, leading to improved generalization.
  arXiv  Detail & Related papers  (2024-06-02T14:43:56Z)
• 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)
• Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization [51.913685334368104]
  We propose a novel graph invariant learning method based on invariant and variant patterns co-mixup strategy. Our method significantly outperforms state-of-the-art under various distribution shifts.
  arXiv  Detail & Related papers  (2023-12-18T07:26:56Z)
• Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization [41.58330883016538]
  We study the out-of-distribution (OOD) generalization on dynamic graphs from the environment learning perspective. We propose a Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex environments and exploiting novel graph-temporalvariant patterns. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.
  arXiv  Detail & Related papers  (2023-11-18T16:31:10Z)
• 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)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• 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)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)

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.