62 citations · 72 across the 6 of their papers we have counts for
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cs.LG2022★ 3 cited
SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP
Jie Chen, Shouzhen Chen, Mingyuan Bai +3
The message-passing mechanism helps Graph Neural Networks (GNNs) achieve remarkable results on various node classification tasks. Nevertheless, the recursive nodes fetching and agg…
cs.LG2022★ 3 cited
Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily
Jie Chen, Shouzhen Chen, Junbin Gao +3
Due to the homophily assumption in graph convolution networks (GNNs), a common consensus in the graph node classification task is that GNNs perform well on homophilic graphs but ma…