Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks

• URL: http://arxiv.org/abs/2308.06960v2
• Date: Fri, 1 Mar 2024 11:52:33 GMT
• Title: Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks
• Authors: Zhili Wang, Shimin Di, Lei Chen, Xiaofang Zhou
• Abstract summary: Graph neural networks (GNNs) have shown their unprecedented success in many graph-related tasks. Recent efforts try to pre-train GNNs on a large-scale unlabeled graph and adapt the knowledge from the unlabeled graph to the target downstream task. Despite the importance of fine-tuning, current GNNs pre-training works often ignore designing a good fine-tuning strategy.
• Score: 22.446655655309854
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recently, graph neural networks (GNNs) have shown its unprecedented success in many graph-related tasks. However, GNNs face the label scarcity issue as other neural networks do. Thus, recent efforts try to pre-train GNNs on a large-scale unlabeled graph and adapt the knowledge from the unlabeled graph to the target downstream task. The adaptation is generally achieved by fine-tuning the pre-trained GNNs with a limited number of labeled data. Despite the importance of fine-tuning, current GNNs pre-training works often ignore designing a good fine-tuning strategy to better leverage transferred knowledge and improve the performance on downstream tasks. Only few works start to investigate a better fine-tuning strategy for pre-trained GNNs. But their designs either have strong assumptions or overlook the data-aware issue for various downstream datasets. Therefore, we aim to design a better fine-tuning strategy for pre-trained GNNs to improve the model performance in this paper. Given a pre-trained GNN, we propose to search to fine-tune pre-trained graph neural networks for graph-level tasks (S2PGNN), which adaptively design a suitable fine-tuning framework for the given labeled data on the downstream task. To ensure the improvement brought by searching fine-tuning strategy, we carefully summarize a proper search space of fine-tuning framework that is suitable for GNNs. The empirical studies show that S2PGNN can be implemented on the top of 10 famous pre-trained GNNs and consistently improve their performance. Besides, S2PGNN achieves better performance than existing fine-tuning strategies within and outside the GNN area. Our code is publicly available at \url{https://anonymous.4open.science/r/code_icde2024-A9CB/}.

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