Graph Machine Learning

<Graph Machine Learning> Build machine learning algorithms using graph data and efficiently exploit topological information within your models

▶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

▶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

▶Who This Book Is For
This book is for data scientists, data analysts, 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 using machine learning. The book will also be useful for machine learning developers or anyone 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.

▶What this book covers
- Chapter 1, Getting Started with Graphs, introduces the basic concepts of graph theory using the NetworkX Python library.

- Chapter 2, Graph Machine Learning, introduces the main concepts of graph machine learning and graph embedding techniques.

- Chapter 3, Unsupervised Graph Learning, covers recent unsupervised graph embedding methods.

- Chapter 4, Supervised Graph Learning, covers recent supervised graph embedding methods.

- Chapter 5, Problems with Machine Learning on Graphs, introduces the most common machine learning tasks on graphs.

- Chapter 6, Social Network Analysis, shows an application of machine learning algorithms on social network data.

- Chapter 7, Text Analytics and Natural Language Processing Using Graphs, shows the application of machine learning algorithms to natural language processing tasks.

- Chapter 8, Graph Analysis for Credit Card Transactions, shows the application of machine learning algorithms to credit card fraud detection.

- Chapter 9, Building a Data-Driven Graph-Powered Application, introduces some technologies and techniques that are useful for dealing with large graphs.

- Chapter 10, Novel Trends on Graphs, introduces some novel trends (algorithms and applications) in graph machine learning.