7 papers
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…
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…
Structure-Centric Graph Foundation Model via Geometric Bases
Xiaodong He, Haolan He, Ruiyi Fang +2
Graph foundation models (GFMs) seek transferable representations across graph domains but are limited by structural heterogeneity and incompatible node feature spaces. We propose S…
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…
When Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation Learning
Zhixiang Shen, Zhao Kang
Unsupervised heterogeneous graph representation learning (UHGRL) has gained increasing attention due to its significance in handling practical graphs without labels. However, heter…