Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

3-2025

Abstract

Few-shot learning has emerged as an important problem on graphs to combat label scarcity, which can be approached by current trends in pre-trained graph neural networks (GNNs) and meta-learning. Recent efforts integrate both paradigms in a white-box setting, leaving the more realistic black-box setting largely underexplored, where the parameters and gradients in the pre-trained GNNs are inaccessible. In this paper, we study the critical problem: Leveraging black-box pre-trained GNNs for graph few-shot learning. Despite its appeal, two key issues hinder the unlocking of its potential: the inherent task gap between pre-training and downstream stages, which can introduce irrelevant knowledge and undermine the generalizability of a pre-trained black-box GNN on downstream tasks; and the inaccessibility of parameters and gradients, which limits the model's adaptation to novel tasks. To effectively leverage the black-box pre-trained GNNs and improve generalization, we propose a lightweight graph meta-learner to extract relevant knowledge from a black-box pre-trained GNN, meanwhile harnessing knowledge from related tasks for rapid adaptation on novel tasks. Furthermore, we prune the graph meta-learner to enhance its generalization on novel tasks. Extensive experiments on real-world datasets for few-shot node classification validate the effectiveness of our proposed method in the black-box setting.

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

AAAI'25/IAAI'25/EAAI'25: Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence and Thirty-Seventh Conference on Innovative Applications of Artificial Intelligence and Fifteenth Symposium on Educational Advances in Artificial Intelligence, Philadelphia, Pennyslvania, February 25 - March 4

First Page

22497

Last Page

22505

Identifier

10.1609/aaai.v39i21.34407

City or Country

Philadelphia, Pennsylvania, USA

Additional URL

https://doi.org/10.1609/aaai.v39i21.34407

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