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graph-attention-nets
keras-io/graph-attention-nets
graph-attention-nets is a machine learning model from keras-io. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for tf-keras. The card lists the license as cc0-1.0.
This repo contains the model and the notebook to this Keras example on Graph Attention Networks for Node Classification.
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At a glance
- Library
- tf-keras
- License
- cc0-1.0
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Jan 13, 2022
- SHA
- 8dc2e8c1
Model card
From the Hugging Face model README
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.
Full credits to: Alexander Kensert
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.
This tutorial implements a specific graph neural network known as a Graph Attention Network (GAT) to predict labels of scientific papers based on the papers they cite (using the Cora dataset).
References For more information on GAT, see the original paper Graph Attention Networks as well as DGL's Graph Attention Networks documentation.
- Library
- tf-keras
- License
- cc0-1.0
- Created
- Mar 2, 2022
- Updated
- Jan 13, 2022