Graph Algorithms for Data Science

Graphs are the natural way to understand connected data, and this book explores the most important algorithms and techniques for graphs in data science, with practical examples and advice on implementation and deployment. It teaches you how to construct graphs from both structured and unstructured data, how to manipulate graph structures with Cypher query language, and how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks.

Format: Paperback / softback
Length: 325 pages
Publication date: 06 February 2024
Publisher: Manning Publications

Graphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment.

In Graph Algorithms for Data Science, you will learn:

Labeled-property graph modeling: Constructing a graph from structured data such as CSV or SQL.

NLP techniques to construct a graph from unstructured data.

Cypher query language syntax to manipulate data and extract insights.

Social network analysis algorithms like PageRank and community detection.

How to translate graph structure to a ML model input with node embedding models.

Using graph features in node classification and link prediction workflows.

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. It's 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. You don't need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that make them easy to apply to your own projects.

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

About the book: Graph Algorithms for Data Science teaches you how to leverage the power of graphs to solve complex data science problems. You'll learn how to construct and manipulate graphs, apply machine learning algorithms to graph data, and use graph visualization techniques to gain insights. With hands-on examples and practical advice, this book will help you take your data science skills to the next level. Whether you're a data scientist, engineer, or business analyst, Graph Algorithms for Data Science is the essential guide for working with graph-based data.

Weight: 654g
Dimension: 235 x 185 x 17 (mm)
ISBN-13: 9781617299469