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arXiv · 2201.08265

Cross-Domain Few-Shot Graph Classification

Kaveh Hassani
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Abstract

We study the problem of few-shot graph classification across domains with nonequivalent feature spaces by introducing three new cross-domain benchmarks constructed from publicly available datasets. We also propose an attention-based graph encoder that uses three congruent views of graphs, one contextual and two topological views, to learn representations of task-specific information for fast adaptation, and task-agnostic information for knowledge transfer. We run exhaustive experiments to evaluate the performance of contrastive and meta-learning strategies. We show that when coupled with metric-based meta-learning frameworks, the proposed encoder achieves the best average meta-test classification accuracy across all benchmarks. The source code and data will be released here: https://github.com/kavehhassani/metagrl

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Kaveh Hassani. 2022-01-20. Cross-Domain Few-Shot Graph Classification. https://arxiv.org/abs/2201.08265

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