Preserving Geometric Integrity in Graph Prompting via Measure-Constrained Optimal Transport
Graph prompt learning enables parameter-efficient adaptation of frozen Graph Neural Networks to downstream tasks through lightweight prompt parameters. As routing becomes increasingly node-adaptive, however, independently optimized local decisions can collectively concentrate assignment mass on a small subset of a finite shared prompt bank, even when individual node--prompt matches remain locally meaningful. We propose MINT (Measure-INtegrity Transport), an entropically regularized optimal transport framework that formulates node-to-prompt adaptation as a globally coupled allocation problem. The transport cost favors local geometric compatibility, while a prescribed prompt-side marginal e...
| Authors | Xiangyu Wang, Shuo Wang, Ruiyi Fang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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