MAGPrompt: Message-Adaptive Graph Prompt Tuning for Graph Neural Networks

• URL: http://arxiv.org/abs/2602.05567v1
• Date: Thu, 05 Feb 2026 11:39:49 GMT
• Title: MAGPrompt: Message-Adaptive Graph Prompt Tuning for Graph Neural Networks
• Authors: Long D. Nguyen, Binh P. Nguyen,
• Abstract summary: Graph neural networks (GNNs) transfer well, but adapting them to downstream tasks is challenging due to mismatches between pre-training objectives and task requirements.<n>We propose message-adaptive graph prompt tuning, which injects learnable prompts into the message passing step to reweight incoming neighbor messages and add task-specific prompt vectors during message aggregation.<n> Experiments on diverse node- and graph-level datasets show consistent gains over prior graph prompting methods in few-shot settings, while achieving performance competitive with fine-tuning in full-shot regimes.
• Score: 1.6242924916178285
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Pre-trained graph neural networks (GNNs) transfer well, but adapting them to downstream tasks remains challenging due to mismatches between pre-training objectives and task requirements. Graph prompt tuning offers a parameter-efficient alternative to fine-tuning, yet most methods only modify inputs or representations and leave message passing unchanged, limiting their ability to adapt neighborhood interactions. We propose message-adaptive graph prompt tuning, which injects learnable prompts into the message passing step to reweight incoming neighbor messages and add task-specific prompt vectors during message aggregation, while keeping the backbone GNN frozen. The approach is compatible with common GNN backbones and pre-training strategies, and applicable across downstream settings. Experiments on diverse node- and graph-level datasets show consistent gains over prior graph prompting methods in few-shot settings, while achieving performance competitive with fine-tuning in full-shot regimes.

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