PacktPublishing/Hands-On-Graph-Neural-Networks-Using-Python resource

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1053 stars · 295 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

The official code repository for the Packt book 'Hands-On Graph Neural Networks Using Python', containing Jupyter Notebook examples for chapters 2-14. It teaches practical implementation of graph neural networks using Python and PyTorch Geometric.

Use cases

• learn graph neural networks from scratch
• implement GNNs with PyTorch Geometric
• classify nodes, edges, and graphs with deep learning
• generate realistic graph topologies
• apply GNNs to recommendation systems and drug discovery
• forecast events using graph topology

When to choose

• you want a structured, book-backed introduction to graph neural networks
• you learn best from runnable Jupyter Notebook examples
• you are a Python or ML practitioner exploring PyTorch Geometric

When to avoid

• you need a production-ready GNN library rather than educational code
• you want a framework or tool to integrate into an application
• you don't have basic Python and machine learning background

Facets

learning-resource · maturity stable

machine-learning deep-learning data-science machine-learning deep-learning tutorials data-science python cross-platform graph-neural-networks pytorch-geometric book-code jupyter-notebooks packt

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