Handling Distribution Shifts on Graphs: An Invariance Perspective

• URL: http://arxiv.org/abs/2202.02466v5
• Date: Fri, 16 Aug 2024 08:25:42 GMT
• Title: Handling Distribution Shifts on Graphs: An Invariance Perspective
• Authors: Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf,
• Abstract summary: 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.
• Score: 78.31180235269035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so that research on out-of-distribution (OOD) generalization comes into the spotlight. Nonetheless, current endeavors mostly focus on Euclidean data, and its formulation for graph-structured data is not clear and remains under-explored, given two-fold fundamental challenges: 1) the inter-connection among nodes in one graph, which induces non-IID generation of data points even under the same environment, and 2) the structural information in the input graph, which is also informative for prediction. In this paper, we formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM), that facilitates graph neural networks to leverage invariance principles for prediction. EERM resorts to multiple context explorers (specified as graph structure editers in our case) that are adversarially trained to maximize the variance of risks from multiple virtual environments. Such a design enables the model to extrapolate from a single observed environment which is the common case for node-level prediction. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution and further demonstrate its power on various real-world datasets for handling distribution shifts from artificial spurious features, cross-domain transfers and dynamic graph evolution.

Related papers

• DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification [14.96980804513399]
  Graph Neural Networks (GNNs) are susceptible to distribution shifts, creating vulnerability and security issues in critical domains. Existing methods that target learning an invariant (feature, structure)-label mapping often depend on oversimplified assumptions about the data generation process. We introduce a more realistic graph data generation model using Structural Causal Models (SCMs) We propose a casual decoupling framework, DeCaf, that independently learns unbiased feature-label and structure-label mappings.
  arXiv  Detail & Related papers  (2024-10-27T00:22:18Z)
• 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)
• Graphs Generalization under Distribution Shifts [11.963958151023732]
  We introduce a novel framework, namely Graph Learning Invariant Domain genERation (GLIDER) Our model outperforms baseline methods on node-level OOD generalization across domains in distribution shift on node features and topological structures simultaneously.
  arXiv  Detail & Related papers  (2024-03-25T00:15:34Z)
• 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)
• Variational Disentangled Graph Auto-Encoders for Link Prediction [10.390861526194662]
  This paper proposes a novel framework with two variants, the disentangled graph auto-encoder (DGAE) and the variational disentangled graph auto-encoder (VDGAE) The proposed framework infers the latent factors that cause edges in the graph and disentangles the representation into multiple channels corresponding to unique latent factors.
  arXiv  Detail & Related papers  (2023-06-20T06:25:05Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• Invariance Principle Meets Out-of-Distribution Generalization on Graphs [66.04137805277632]
  Complex nature of graphs thwarts the adoption of the invariance principle for OOD generalization. domain or environment partitions, which are often required by OOD methods, can be expensive to obtain for graphs. We propose a novel framework to explicitly model this process using a contrastive strategy.
  arXiv  Detail & Related papers  (2022-02-11T04:38:39Z)
• Discovering Invariant Rationales for Graph Neural Networks [104.61908788639052]
  Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features. We propose a new strategy of discovering invariant rationale (DIR) to construct intrinsically interpretable GNNs.
  arXiv  Detail & Related papers  (2022-01-30T16:43:40Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)

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.