Invariance Principle Meets Out-of-Distribution Generalization on Graphs
- URL: http://arxiv.org/abs/2202.05441v1
- Date: Fri, 11 Feb 2022 04:38:39 GMT
- Title: Invariance Principle Meets Out-of-Distribution Generalization on Graphs
- Authors: Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie,
Tongliang Liu, Bo Han, James Cheng
- Abstract summary: 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.
- Score: 66.04137805277632
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Despite recent developments in using the invariance principle from causality
to enable out-of-distribution (OOD) generalization on Euclidean data, e.g.,
images, studies on graph data are limited. Different from images, the complex
nature of graphs poses unique challenges that thwart the adoption of the
invariance principle for OOD generalization. In particular, distribution shifts
on graphs can happen at both structure-level and attribute-level, which
increases the difficulty of capturing the invariance. Moreover, domain or
environment partitions, which are often required by OOD methods developed on
Euclidean data, can be expensive to obtain for graphs. Aiming to bridge this
gap, we characterize distribution shifts on graphs with causal models, and show
that the OOD generalization on graphs with invariance principle is possible by
identifying an invariant subgraph for making predictions. We propose a novel
framework to explicitly model this process using a contrastive strategy. By
contrasting the estimated invariant subgraphs, our framework can provably
identify the underlying invariant subgraph under mild assumptions. Experiments
across several synthetic and real-world datasets demonstrate the
state-of-the-art OOD generalization ability of our method.
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