6 citations · 8 across the 7 of their papers we have counts for
4 papers · 1 filter
Preserving Geometric Integrity in Graph Prompting via Measure-Constrained Optimal Transport
Xiangyu Wang, Shuo Wang, Ruiyi Fang +1
Graph prompt learning enables parameter-efficient adaptation of frozen Graph Neural Networks to downstream tasks through lightweight prompt parameters. As routing becomes increasin…
Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts
Quanxin Wang, Xuanting Xie, Bingheng Li +4
Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts. However, existing methods operate within a…
The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning
Shuo Wang, Xiangyu Wang, Quanxin Wang +9
Current evaluation practices in relational learning rely heavily on flat leaderboards that average performance across heterogeneous datasets, implicitly assuming a uniform underlyi…
Maximising the Set-Piece Return: Optimising Football Corner Tactics with Graph Reinforcement Learning
Sean Groom, Michael Groom, Francisco Belo +4
Machine learning is increasingly employed for the evaluation of football tactics. However, existing approaches focus on characterising historical actions or analyst-specified count…