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

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