• Corpus ID: 237492193

# Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?

@article{Luan2021IsHA,
title={Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?},
author={Sitao Luan and Chenqing Hua and Qincheng Lu and Jiaqi Zhu and Mingde Zhao and Shuyuan Zhang and Xiao-Wen Chang and Doina Precup},
journal={ArXiv},
year={2021},
volume={abs/2109.05641}
}
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 believed to outperform NNs in real-world tasks, performance advantages of GNNs over graph-agnostic NNs seem not generally satisfactory. Heterophily has been considered as a main cause and numerous works have been put forward to address it. In this paper, we first show that not all cases of heterophily are harmful 1 for GNNs…
1 Citations

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