To get the most out of this book
To make the most of this book, you should have a foundation in machine learning fundamentals, basic graph theory, and programming concepts. We assume you’re familiar with Python programming and have a working understanding of neural networks and deep learning principles. Prior exposure to data structures and algorithms will be beneficial but is not mandatory.
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Software/hardware covered in the book |
Operating system requirements |
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Jupyter Lab/Google Colab |
Windows, macOS, or Linux |
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Python |
To successfully work with the examples in this book, you’ll need to set up your Python environment with several essential packages. Start by installing the core libraries:
- PyTorch for deep learning operations
- PyTorch Geometric for graph-based neural networks
- NetworkX for graph manipulation and analysis
- NumPy and pandas for data handling and numerical computations
These can be installed using pip or conda package managers.
For optimal performance, ensure your system has at least 8 GB of RAM and a relatively modern CPU. While a GPU isn’t strictly necessary for running smaller examples, having one will significantly speed up the training process for larger models and more complex graph operations.
If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.
