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Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
[article]
2021
arXiv
pre-print
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 for GNNs
arXiv:2109.05641v1
fatcat:nhgxphswevd3zlcy5ykejt33ha