arXiv:2505.16903v2 Announce Type: replace Abstract: Prompt tuning has become a key mechanism for adapting pre-trained Graph Neural Networks (GNNs) to new downstream tasks. However, existing approaches are predominantly supervised, relying on labeled data to optimize the prompting parameters and typically fine-tuning a task-specific prediction head -- practices that undermine the promise of parameter-efficient adaptation. We propose Unsupervised Graph Prompting Problem (UGPP), a challenging new setting where the pre-trained GNN is kept entirely frozen, labels on the target domain are unavailabl
The rapid deployment of pre-trained large models necessitates more efficient and unsupervised adaptation techniques as traditional supervised fine-tuning becomes cost-prohibitive and data-intensive.
This framework addresses a critical limitation in Graph Neural Network (GNN) deployment, enabling broader application without requiring extensive labeled datasets for every new task.
Prompt tuning for GNNs can now move towards more parameter-efficient, unsupervised adaptation, significantly reducing the dependency on task-specific labeled data and re-training efforts.
- · AI developers
- · GNN applications (e.g., drug discovery, social network analysis)
- · Cloud computing providers (reduced processing needs for fine-tuning)
- · Tasks requiring extensive manual data labeling
- · Traditional supervised GNN fine-tuning methods
More widespread and cost-effective deployment of GNNs across various industries.
Accelerated development of new GNN applications in domains with scarce labeled data.
Increased automation in data science workflows as model adaptation becomes less human-intensive.
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Read at arXiv cs.LG