Attention-based Graph Neural Network for Semi-supervised Learning

arXiv: Machine Learning, Volume abs/1803.03735, 2018.

Cited by: 80|Bibtex|Views237
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Recently popularized graph neural networks achieve the state-of-the-art accuracy on a number of standard benchmark datasets for graph-based semi-supervised learning, improving significantly over existing approaches. These architectures alternate between a propagation layer that aggregates the hidden states of the local neighborhood and a ...More

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