Cross-Domain Few-Shot Graph Classification

• URL: http://arxiv.org/abs/2201.08265v1
• Date: Thu, 20 Jan 2022 16:16:30 GMT
• Title: Cross-Domain Few-Shot Graph Classification
• Authors: Kaveh Hassani
• Abstract summary: We study the problem of few-shot graph classification across domains with nonequivalent feature spaces. We propose an attention-based graph encoder that uses three congruent views of graphs, one contextual and two topological views. We show that when coupled with metric-based meta-learning frameworks, the proposed encoder achieves the best average meta-test classification accuracy.
• Score: 7.23389716633927
• License: http://creativecommons.org/licenses/by/4.0/
• 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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