To get the most out of this book

We recommend that you use Docker to have a reproducible environment and stable dependency sets. The provided Docker images – one for each chapter – ship with a Jupyter installation and a Python kernel with the dependencies pre-installed, which you can use to run all the examples. For some chapters, Neo4j, JanusGraph, and Gephi are also needed.

A beginner-level understanding of graph databases and graph data is required. Intermediate-level working knowledge of Python programming and machine learning is also expected to make the most of this book.

The authors acknowledge the use of cutting-edge AI, such as ChatGPT, with the sole aim of enhancing the language and clarity within the book, thereby ensuring a smooth reading experience for readers. It’s important to note that the content itself has been crafted by the authors and edited by a professional publishing team.

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/PacktPublishing/Graph-Machine-Learning. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and X/Twitter handles. For example: “For this exercise, we will be using a GraphSAGE encoder with three layers of 32, 32, and 16 dimensions, respectively.”

A block of code is set as follows:

TMF_model = TMF(num_nodes, hid_dim, win_size, num_epochs, alpha, beta, theta, learn_rate, device)
adj_est = TMF_model.TMF_fun(adj_list)

Any command-line input or output is written as follows:

Precision: 0.9636952636282395
Recall: 0.9777853337866939
F1-Score: 0.9706891701828411