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README.md

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+---
+thumbnail: "url to a thumbnail used in social sharing"
+tags:
+- graph neural networks
+license:
+- cc0.0
+---
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+## Keras Implementation of Graph attention networks for node classification 🕸
+
+This repo contains the model and the notebook [to this Keras example on Graph Attention Networks for Node Classification](https://keras.io/examples/graph/gat_node_classification/).
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+Full credits to: [Alexander Kensert]/(https://github.com/akensert)
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+## Background Information 
+Graph neural networks is the preferred neural network architecture for processing data structured as graphs (for example, social networks or molecule structures), yielding better results than fully-connected networks or convolutional networks.
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+This tutorial implements a specific graph neural network known as a [Graph Attention Network (GAT)](https://arxiv.org/abs/1710.10903) to predict labels of scientific papers based on the papers they cite (using the [Cora dataset](https://linqs.soe.ucsc.edu/data)).
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+References
+For more information on GAT, see the original paper [Graph Attention Networks](https://arxiv.org/abs/1710.10903) as well as [DGL's Graph Attention Networks](https://docs.dgl.ai/en/0.4.x/tutorials/models/1_gnn/9_gat.html) documentation.