Graphs: The natural way to understand data

Graphs reveal the relationships in your data. Tracking these connections reveals new insights and influences and lets you analyze each data point as part of a larger whole. This interconnected data is ideal for machine learning, as well as analyzing social networks, communities, and even product recommendations.

Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. Filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. 

You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. No prior graph experience is needed to start benefiting from this insightful guide. Powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.

"This book gave me a fundamental framework on how to think about solving problems using graph algorithms."

—Subhash Talluri

This book will teach you how to construct graphs from both structured and unstructured data. You’ll see how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. 

Explore common and useful graph algorithms & instantly put them into action to complete hands-on data projects! Finally, you’ll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks.

Graph Algorithms for Data Science  is available from its publisher Manning Publications.

 
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