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Author

Shuo Wang

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#machine learning Preprint Sep 2026

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 fini...

Xiang-Yu Wang, Shuo Wang, Rui-Yi Fang et al. · 0 citations
Preprint Aug 2026

Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts

CurvPrompt is proposed, a topology-routed geometry prompting framework for dynamic graphs that significantly advances few-shot link prediction while delivering strong, consistent performance on node classification tasks, validating the necessity of geometry-adaptive prompting.

Quanxin Wang, Xuanting Xie, Bingheng Li et al. · 2 citations

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