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cs.LG2024
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…
cs.LG2024
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…
cs.LG2024
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…