MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing

30 Apr 2019Sami Abu-El-HaijaBryan PerozziAmol KapoorNazanin AlipourfardKristina LermanHrayr HarutyunyanGreg Ver SteegAram Galstyan

Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships. To address this weakness, we propose a new model, MixHop, that can learn these relationships, including difference operators, by repeatedly mixing feature representations of neighbors at various distances... (read more)

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