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Notebook","readme":"# Introduction to Graph Neural Networks and Explainability\n\nThis 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.\nThe tutorial will also cover some streamlined interpretability tools for analyzing the decision process of a GNN.\n\nThe tutorial is a Python notebook that you can\n\n[![nbviewer](https://img.shields.io/badge/-View-blue?logo=jupyter\u0026style=flat\u0026labelColor=gray)](https://nbviewer.org/github/FilippoMB/Tutorial_GNN_explainability/blob/main/tutorial.ipynb) \nor [![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)\n\n---\nTo run the notebook locally, you need the following libraries:\n\n```\n- PyTorch\n- PyTorch Geometric\n- Pytorch Lightning\n- Captum\n- Networkx\n```\n---\n\nThis 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)).\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffilippomb%2Ftutorial_gnn_explainability","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffilippomb%2Ftutorial_gnn_explainability","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffilippomb%2Ftutorial_gnn_explainability/lists"}