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--- |
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thumbnail: "url to a thumbnail used in social sharing" |
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tags: |
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- graph neural networks |
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license: |
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- cc0-1.0 |
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--- |
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## Keras Implementation of Graph Attention Networks for Node Classification 🕸 |
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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 |
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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 |
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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. |
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