Semi-Supervised Classification with Graph Convolutional Networks
Authors: 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.
Tools for this paper
Ask questions about this paper to our AI assistant
You can also chat with multiple papers at once here.
⚠The license of the paper does not allow us to build upon its content and the AI assistant only knows about the paper metadata rather than the full article.
The previous summary was created more than a year ago and can be re-run (if necessary) by clicking on the Run button below.
⚠The license of this specific paper does not allow us to build upon its content and the summarizing tools will be run using the paper metadata rather than the full article. However, it still does a good job, and you can also try our tools on papers with more open licenses.
Look for similar papers (in beta version)
We compare the title and abstract with papers indexed in our database. For arXiv papers, we search within the same category; for DOI and other sources, we search across sources and subjects. You can rerun the search as our database grows.
Disclaimer: The AI-based summarization tool and virtual assistant provided on this website may not always provide accurate and complete summaries or responses. We encourage you to carefully review and evaluate the generated content to ensure its quality and relevance to your needs.