most citedFinding Global Homophily in Graph Neural Networks When Meeting Heterophily

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cs.LG2022★ 38 cited

Finding Global Homophily in Graph Neural Networks When Meeting Heterophily

Xiang Li, Renyu Zhu, Yao Cheng +4

We investigate graph neural networks on graphs with heterophily. Some existing methods amplify a node's neighborhood with multi-hop neighbors to include more nodes with homophily.…