12 citations · 18 across the 6 of their papers we have counts for
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cs.LG2024★ 2 cited
The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs
Kun Wang, Guibin Zhang, Xinnan Zhang +6
Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based learning tasks. Notably, most current GNN architectures operate under the assumption of homophily,…
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…