https://github.com/filippomb/tutorial_gnn_explainability

This in an introduction to PyTorch Geometric, the deep learning library for Graph Neural Networks, and to interpretability tools for analyzing the decision process of a GNN.
https://github.com/filippomb/tutorial_gnn_explainability

captum explainability explainable-ai geometric-deep-learning graph-neural-networks pytorch-geometric tutorial tutorial-code

Last synced: 3 months ago
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This in an introduction to PyTorch Geometric, the deep learning library for Graph Neural Networks, and to interpretability tools for analyzing the decision process of a GNN.

• Host: GitHub
• URL: https://github.com/filippomb/tutorial_gnn_explainability
• Owner: FilippoMB
• License: mit
• Created: 2023-11-22T13:19:50.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2025-03-15T10:34:57.000Z (11 months ago)
• Last Synced: 2025-03-15T11:27:10.368Z (11 months ago)
• Topics: captum, explainability, explainable-ai, geometric-deep-learning, graph-neural-networks, pytorch-geometric, tutorial, tutorial-code
• Language: Jupyter Notebook
• Homepage:
• Size: 2.72 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Introduction to Graph Neural Networks and Explainability

This is a gentle introduction to building a Graph Neural Network with [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/), the popular library for geometric deep learning.

The tutorial will also cover some streamlined interpretability tools for analyzing the decision process of a GNN.

The tutorial is a Python notebook that you can

[![nbviewer](https://img.shields.io/badge/-View-blue?logo=jupyter&style=flat&labelColor=gray)](https://nbviewer.org/github/FilippoMB/Tutorial_GNN_explainability/blob/main/tutorial.ipynb) 

or [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/FilippoMB/Tutorial_GNN_explainability/blob/main/tutorial.ipynb)

---

To run the notebook locally, you need the following libraries:

```

- PyTorch

- PyTorch Geometric

- Pytorch Lightning

- Captum

- Networkx

```

---

This tutorial is adapted from the tutorial of [Simone Scardapane](https://www.sscardapane.it/) ([slides](https://docs.google.com/presentation/d/103YA-dJ9PO9iSyUxoY-xbJhVu6VrEsMvvw4rBe6QBwY/edit#slide=id.g1e3b33a1319_0_0), [notebook](https://colab.research.google.com/drive/1nV44NrNqcXC2thU6-zzxnJPnIalo870m)).