Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms

Build machine learning algorithms using graph data and efficiently exploit topological information within your models
Key Features

Implement machine learning techniques and algorithms in graph data
Identify the relationship between nodes in order to make better business decisions
Apply graph-based machine learning methods to solve real-life problems

Book Description

Graph Machine Learning provides a new set of tools for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks.

You will start with a brief introduction to graph theory and graph machine learning, understanding their potential. As you proceed, you will become well versed with the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll then build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. Moving ahead, you will cover real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. Finally, you will learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, before progressing to explore the latest trends on graphs.

By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications.
What you will learn

Write Python scripts to extract features from graphs
Distinguish between the main graph representation learning techniques
Become well-versed with extracting data from social networks, financial transaction systems, and more
Implement the main unsupervised and supervised graph embedding techniques
Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more
Deploy and scale out your application seamlessly

Who this book is for

This book is for data analysts, graph developers, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance. The book will also be useful for data scientists and machine learning developers who want to build ML-driven graph databases. 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 out of this book.
Table of Contents

Getting Started with Graphs
Graph Machine Learning
Unsupervised Graph Learning
Supervised Graph Learning
Problems with Machine Learning on Graphs
Social Network Graphs
Text Analytics and Natural Language Processing Using Graphs
Graph Analysis for Credit Card Transactions
Building a Data-Driven Graph-Powered Application
Novel Trends on Graphs