This repository provides code examples for the "Graph Machine Learning" book by Packt Publishing. It targets data analysts, graph developers, and data scientists seeking to leverage graph data and relationships for enhanced analysis and model performance, offering practical Python scripts for feature extraction, embedding techniques, and graph neural networks.

How It Works

The book and its accompanying code demonstrate various graph machine learning techniques, including shallow embedding methods, graph neural networks, and regularization. It guides users through extracting features from diverse graph datasets like social networks and financial transactions, implementing both unsupervised and supervised embedding approaches. The approach focuses on practical application and understanding the underlying methodologies for predictive modeling and analytics.

Quick Start & Requirements

• Installation: Primarily uses Python with libraries like stellargraph. Code examples are organized by chapter.
• Prerequisites: Python (any version), Neo4j, Gephi, and Google Colab or Jupyter Notebook are required for chapters 1-10. An intermediate understanding of graph databases, graph data, Python, and machine learning is expected.
• Resources: No specific hardware requirements are listed beyond standard OS compatibility (Windows, macOS, Linux).

Highlighted Details

• Covers feature extraction from graphs using Python.
• Distinguishes between main graph representation learning techniques.
• Implements unsupervised and supervised graph embedding methods.
• Explores shallow embedding, graph neural networks, and regularization.

Maintenance & Community

This repository is associated with a published book. Information on ongoing maintenance or community support channels (like Discord/Slack) is not provided in the README. The authors are Claudio Stamile, Aldo Marzullo, and Enrico Deusebio, with backgrounds in AI, graph theory, and machine learning.

Licensing & Compatibility

The repository itself does not explicitly state a license. However, as it's tied to a Packt Publishing book, usage of the code is likely governed by the book's terms and conditions. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The README contains an errata for a code snippet (nt.to.numpy.matrix(G) should be nx.to.numpy.matrix(G)). The project is presented as code examples for a book, implying it may not be a continuously maintained library or framework.