arXiv (Cornell University) / 2016

Semi-Supervised Classification with Graph Convolutional Networks

Thomas Kipf, Max Welling

Graph LearningPopular and Landmark Papers

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.

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### Paper attached as context
Title: Semi-Supervised Classification with Graph Convolutional Networks
Authors: Thomas Kipf, Max Welling
Year: 2016
Venue: arXiv (Cornell University)
Categories: Graph Learning, Popular and Landmark Papers
Citations: 8,068
Paper URL: https://arxiv.org/abs/1609.02907
Open PDF: https://arxiv.org/pdf/1609.02907

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.

Learning resources:
- PDF: arXiv PDF (https://arxiv.org/pdf/1609.02907)
- arXiv: arXiv abstract page (https://arxiv.org/abs/1609.02907)
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=Semi-Supervised%20Classification%20with%20Graph%20Convolutional%20Networks)
- OpenAlex: Paper page (http://arxiv.org/abs/1609.02907)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=Semi-Supervised%20Classification%20with%20Graph%20Convolutional%20Networks)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=Semi-Supervised%20Classification%20with%20Graph%20Convolutional%20Networks+paper+explained)