260 citations · 371 across the 8 of their papers we have counts for
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cs.LG2023★ 3 cited
The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field
Kun Wang, Guohao Li, Shilong Wang +6
Despite Graph Neural Networks demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with over-fitting and over-smoot…
cs.LG2021★ 8 cited
Training Graph Neural Networks with 1000 Layers
Guohao Li, Matthias Müller, Bernard Ghanem +1
Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges. However, memory complexity…
cs.LG2020★ 260 cited
DeeperGCN: All You Need to Train Deeper GCNs
Guohao Li, Chenxin Xiong, Ali Thabet +1
Graph Convolutional Networks (GCNs) have been drawing significant attention with the power of representation learning on graphs. Unlike Convolutional Neural Networks (CNNs), which…