Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
arxiv(2024)
Abstract
Heterogeneous graph few-shot learning (HGFL) has been developed to address
the label sparsity issue in heterogeneous graphs (HGs), which consist of
various types of nodes and edges. The core concept of HGFL is to extract
knowledge from rich-labeled classes in a source HG, transfer this knowledge to
a target HG to facilitate learning new classes with few-labeled training data,
and finally make predictions on unlabeled testing data. Existing methods
typically assume that the source HG, training data, and testing data all share
the same distribution. However, in practice, distribution shifts among these
three types of data are inevitable due to two reasons: (1) the limited
availability of the source HG that matches the target HG distribution, and (2)
the unpredictable data generation mechanism of the target HG. Such distribution
shifts result in ineffective knowledge transfer and poor learning performance
in existing methods, thereby leading to a novel problem of out-of-distribution
(OOD) generalization in HGFL. To address this challenging problem, we propose a
novel Causal OOD Heterogeneous graph Few-shot learning model, namely COHF. In
COHF, we first characterize distribution shifts in HGs with a structural causal
model, establishing an invariance principle for OOD generalization in HGFL.
Then, following this invariance principle, we propose a new variational
autoencoder-based heterogeneous graph neural network to mitigate the impact of
distribution shifts. Finally, by integrating this network with a novel
meta-learning framework, COHF effectively transfers knowledge to the target HG
to predict new classes with few-labeled data. Extensive experiments on seven
real-world datasets have demonstrated the superior performance of COHF over the
state-of-the-art methods.
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