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!

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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

About the Author

Alessandro Negro is immensely passionate about computer science, data research & specialize in natural language processing, recommendation engines, fraud detection, and graph-aided search.

After pursuing Computer Engineering academically & working in various capacities in the domain, he pursued a Ph.D in Interdisciplinary Science and Technology. With his interest in graph databases peaking, he founded a company called Reco4 which was an open source project developing a recommendation framework based on graph data sources.

Now, he is Chief Scientist at GraphAware. With clients such as LinkedIn, World Economic Forum, European Space Agency and Bank of America, GraphAware is singularly focused on helping clients gain a competitive edge by transforming their data into searchable, understandable & actionable knowledge. He spend his time leading research for Hume - GraphAware Knowledge Graph platform -, speaking at various conferences around the world and working on the next book.

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