3 citations · 5 across the 5 of their papers we have counts for
5 papers
Rethinking Structure Learning For Graph Neural Networks
Yilun Zheng, Zhuofan Zhang, Ziming Wang +4
To improve the performance of Graph Neural Networks (GNNs), Graph Structure Learning (GSL) has been extensively applied to reconstruct or refine original graph structures, effectiv…
Is Graph Convolution Always Beneficial For Every Feature?
Yilun Zheng, Xiang Li, Sitao Luan +2
Graph Neural Networks (GNNs) have demonstrated strong capabilities in processing structured data. While traditional GNNs typically treat each feature dimension equally during graph…
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
Sitao Luan, Chenqing Hua, Qincheng Lu +11
Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of…
What Is Missing In Homophily? Disentangling Graph Homophily For Graph Neural Networks
Yilun Zheng, Sitao Luan, Lihui Chen
Graph homophily refers to the phenomenon that connected nodes tend to share similar characteristics. Understanding this concept and its related metrics is crucial for designing eff…
Representation Learning on Heterophilic Graph with Directional Neighborhood Attention
Qincheng Lu, Jiaqi Zhu, Sitao Luan +1
Graph Attention Network (GAT) is one of the most popular Graph Neural Network (GNN) architecture, which employs the attention mechanism to learn edge weights and has demonstrated p…