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Handling Distribution Shifts on Graphs: An Invariance Perspective

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arxiv 2202.02466 v5 pith:G5CZRWKU submitted 2022-02-05 cs.LG cs.AI

Qitian Wu , Hengrui Zhang , Junchi Yan , David Wipf This is my paper
classification cs.LGcs.AI
keywords graphdatadistributionpredictionshiftscaseeermenvironment
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Recipe for Causal Graph Regression: Confounding Effects Revisited

    cs.LG 2025-07 conditional novelty 6.0 of 10

    The paper proposes a contrastive-learning-based causal graph regression framework that explicitly models the predictive power of confounding subgraphs and achieves state-of-the-art OOD generalization on graph regressi...

  2. Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization

    cs.LG 2025-06 conditional novelty 6.0 of 10

    PrunE prunes spurious edges with a size constraint and an epsilon-probability alignment, reporting state-of-the-art graph OOD results despite a weak theoretical analysis.

  3. An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks

    cs.LG 2025-05 reject novelty 5.0 of 10

    GOOD-MIA combines invariant risk minimization, a graph information bottleneck, and risk extrapolation to run membership inference attacks against graph neural networks across different data domains.

  4. Graph Neural Networks for Scalable and Transferable Node Centrality Approximation

    cs.LG 2026-07 conditional novelty 4.0 of 10

    Mixed-distribution GNN training improves transferable betweenness rankings (τ≈0.86–0.92 across families) with large inference speedups, while closeness remains highly topology-sensitive.

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