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arXiv:1609.02907cs.LG

Semi-Supervised Classification with Graph Convolutional Networks

Thomas N. Kipf, Max Welling

Abstract

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.

arXiv comments
Published as a conference paper at ICLR 2017
Journal reference
DOI
Subjects
cs.LGMachine Learning stat.MLMachine Learning (Statistics)

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  1. Ask HN: ML Papers to Implement 1 comment
    • I have done this a few times now. Alone (e.g. https://github.com/paulmorio/geo2dr) and in collaboration with others (e.g. https://github.com/benedekrozemberczki/pytorch_geometric_tem...) primarily as a way to learn about the methods I was interested in from a research…

      paulmorioView on HNParent context

    • Is this a simplified interpretation of GNNs? because I couldn't find a reference for that. EDIT: found it! https://arxiv.org/pdf/1609.02907.pdf

      benibrazView on HNParent context

    • It looks like they go from tree -> sequence via prefix notation. I'm curious why Lample decided on this seq2seq approach when it seems that there might be models which could be more naturally applied to the tree structure directly [1, 2] [1] https://arxiv.org/abs/1902.07282 (an…

      orange3xchickenView on HNParent context

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