Cross-Domain Few-Shot Graph Classification with a Reinforced Task Coordinator

Qiannan Zhang, Shichao Pei, Yang Qiang, Chuxu Zhang, Nitesh V. Chawla, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Cross-domain graph few-shot learning attempts to address the prevalent data scarcity issue in graph mining problems. However, the utilization of cross-domain data induces another intractable domain shift issue which severely degrades the generalization ability of cross-domain graph few-shot learning models. The combat with the domain shift issue is hindered due to the coarse utilization of source domains and the ignorance of accessible prompts. To address these challenges, in this paper, we design a novel Cross-domain Task Coordinator to leverage a small set of labeled target domain data as prompt tasks, then model the association and discover the relevance between meta-tasks from the source domain and the prompt tasks. Based on the discovered relevance, our model achieves adaptive task selection and enables the optimization of a graph learner using the selected fine-grained meta-tasks. Extensive experiments conducted on molecular property prediction benchmarks validate the effectiveness of our proposed method by comparing it with state-of-the-art baselines.
Original languageEnglish (US)
Title of host publicationProceedings of the AAAI Conference on Artificial Intelligence
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages9
StatePublished - Jun 26 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-07-04
Acknowledgements: The research reported in this paper was partially supported by funding from the King Abdullah University of Science and Technology (KAUST).


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