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MixHop: Higher-Order Graph Convolution Architectures via Sparsified Neighborhood Mixing.

arXiv: Learning, (2019)

Cited by: 130|Views101
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Abstract

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 m...More

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