/graph-neural-network

Tutorial on deep learning models that can work with graph data

Primary LanguageJupyter Notebook

Graph Neural Network Examples

Tutorial on deep learning models that can work with graph data

This repository contains a collection of Jupyter Notebook examples demonstrating various applications of Graph Neural Networks (GNNs). Each notebook focuses on a specific task or concept related to GNNs.

Colab Notebooks

  1. Graph Introduction
  2. Karate Club
  3. GNN Citations Node Classification (Keras)
  4. Introduction to PyTorch Geometric
  5. GCN Karate Club with PyTorch Geometric
  6. GNN Citations Node Classification with PyTorch Geometric
  7. Graph Classification using PyTorch Geometric
  8. Graph Regression using PyTorch Geometric
  9. Cluster GCN
  10. GAE Graph Autoencoder
  11. Link Prediction on MovieLens (Part 1)
  12. Link Regression on MovieLens (Part 2)
  13. Spatio-temporal Traffic Forecasting

Usage

To run these notebooks, you can either clone this repository and execute them locally or click on the Colab links above to run them directly in Google Colab.

Code References:

  • [3] Keras Graph Neural Network Citations Example - Available at: link
  • [4] Udemy Course on Graph Neural Network - Available at: link
  • [5] PyG Official Examples - Available at: link
  • [6] PyG Official Examples - Available at: link
  • [6] PyG Official Examples - Available at: link

Please note that the links provided are for reference purposes and may have been updated or changed since the time of writing. You should visit the respective websites to access the most up-to-date information and code examples.