Graph-Powered Machine Learning

Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data.

Summary

In Graph-Powered Machine Learning, you will learn:

• The lifecycle of a machine learning project
• Graphs in big data platforms
• Data source modeling using graphs
• Graph-based natural language processing, recommendations, and fraud detection techniques
• Graph algorithms
• Working with Neo4J

Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro’s extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients!

About the technology

Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems.

About the book

Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks.

What’s inside

• Graphs in big data platforms
• Recommendations, natural language processing, fraud detection
• Graph algorithms
• Working with the Neo4J graph database

About the reader

For readers comfortable with machine learning basics.

About the author

Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science.

Table of Contents

PART 1 INTRODUCTION

1. Machine learning and graphs: An introduction

2. Graph data engineering

3. Graphs in machine learning applications

PART 2 RECOMMENDATIONS

4. Content-based recommendations

5.Collaborative filtering

6. Session-based recommendations

7. Context-aware and hybrid recommendations

PART 3 FIGHTING FRAUD

8. Basic approaches to graph-powered fraud detection

9. Proximity-based algorithms

10. Social network analysis against fraud

PART 4 TAMING TEXT WITH GRAPHS

11. Graph-based natural language processing

12. Knowledge graphs

Review

Quotes:

“I learned so much from this unique and comprehensive book. A real

gem for anyone who wants to explore graph-powered ML apps.”

—Helen Mary Labao-Barrameda, Okada Manila

“The single best source of information for graph-based machine

learning.”

—Odysseas Pentakalos, SYSNET International, Inc

“I learned a lot. Plenty of ‘aha!’ moments.”

—Jose San Leandro Armendáriz, OSOCO.es

“Covers all of the bases and enough real-world examples for you to

apply the techniques to your own work.”

—Richard Vaughan, Purple Monkey Collective

From the Author

Graph-Powered Machine Learning is a practical guide to using graphs effectively in machine learning applications, showing you all the stages of building complete solutions in which graphs play a key role. It focuses on methods, algorithms, and design patterns related to graphs. Based on my experience in building complex machine learning applications, this book suggests many recipes in which graphs are the main ingredient of a tasty product for your customers. Across the life cycle of a machine learning project, such approaches can be useful in several aspects, such as managing data sources more efficiently, implementing better algorithms, storing prediction models so that they can be accessed faster, and visualizing the results in a more effective way for further analysis.