70 citations · 115 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning
Qincheng Lu, Jiaqi Zhu, Sitao Luan +1
The ability of Graph Neural Networks (GNNs) to capture long-range and global topology information is limited by the scope of conventional graph Laplacian, leading to unsatisfactory…
cs.LG2022★ 70 cited
Revisiting Heterophily For Graph Neural Networks
Sitao Luan, Chenqing Hua, Qincheng Lu +5
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using graph structures based on the relational inductive bias (homophily assumption). While GNNs have been common…
cs.LG2021★ 44 cited
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
Sitao Luan, Chenqing Hua, Qincheng Lu +5
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using the graph structures based on the relational inductive bias (homophily assumption). Though GNNs are believe…