6 citations · 8 across the 7 of their papers we have counts for
7 papers
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…
Cooperation of Experts: Fusing Heterogeneous Information with Large Margin
Shuo Wang, Shunyang Huang, Jinghui Yuan +2
Fusing heterogeneous information remains a persistent challenge in modern data analysis. While significant progress has been made, existing approaches often fail to account for the…
Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment
Shuo Wang, Bokui Wang, Zhixiang Shen +2
Recent advances in CV and NLP have inspired researchers to develop general-purpose graph foundation models through pre-training across diverse domains. However, a fundamental chall…
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning
Zhixiang Shen, Shuo Wang, Zhao Kang
Unsupervised Multiplex Graph Learning (UMGL) aims to learn node representations on various edge types without manual labeling. However, existing research overlooks a key factor: th…