Improving Graph Few-shot Learning with Hyperbolic Space and Denoising Diffusion

• URL: http://arxiv.org/abs/2604.27462v1
• Date: Thu, 30 Apr 2026 06:02:16 GMT
• Title: Improving Graph Few-shot Learning with Hyperbolic Space and Denoising Diffusion
• Abstract summary: Graph few-shot learning focuses on effectively learning from only a small number of labeled nodes to quickly adapt to new tasks.<n>We propose IMPRESS, a novel framework that IMproves graPh few-shot learning with hypeRbolic spacE and denoiSing diffuSion.
• Score: 29.072947887330585
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Graph few-shot learning, which focuses on effectively learning from only a small number of labeled nodes to quickly adapt to new tasks, has garnered significant research attention. Despite recent advances in graph few-shot learning that have demonstrated promising performance, existing methods still suffer from several key limitations. First, during the meta-training phase, these methods typically perform node representation learning in Euclidean space, which often fails to capture the inherently hierarchical structure existing in real-world graph data. Second, during the meta-testing phase, they usually fit an empirical target distribution derived from only a few support samples, even when this distribution significantly deviates from the true underlying distribution. To address these issues, we propose IMPRESS, a novel framework that IMproves graPh few-shot learning with hypeRbolic spacE and denoiSing diffuSion. Specifically, our model learns node representations in a hyperbolic space and enriches the support distribution through denoising diffusion mechanisms. Theoretically, IMPRESS achieves a tighter generalization bound. Empirically, IMPRESS consistently outperforms competitive baselines across multiple benchmark datasets.

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