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

Hands-On Graph Neural Networks Using Python, published by Packt

Repository: https://github.com/PacktPublishing/Hands-On-Graph-Neural-Networks-Using-Python
Canonical: https://ross.abutalabs.com/products/hands-on-graph-neural-networks-using-python
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-05-07T12:21:12+00:00

## Health v2 (maintenance only)
Score: 69/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 81, release rhythm 35, longevity 100
- inputs: {"age_days": 1566, "days_push": 118, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1053, forks 295 (observed 2026-08-28T04:03:23.824704+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, deep-learning, tutorials, data-science
- platform: python, cross-platform
- tags: graph-neural-networks, pytorch-geometric, book-code, jupyter-notebooks, packt

## Member repositories
- PacktPublishing/Hands-On-Graph-Neural-Networks-Using-Python (main) score 69

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:23.824704+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:59:19.588678+00:00, confidence not recorded.
  - readme: https://github.com/PacktPublishing/Hands-On-Graph-Neural-Networks-Using-Python (fetched 2026-08-28T04:03:23.824704+00:00, sha c8c2859a118f)
- Data as of 2026-08-30T08:39:29.467469+00:00.