Graph Algorithms for Data Science you own this product

With examples in Neo4j
Tomaž Bratanič
Foreword by Michael Hunger
  • January 2024
  • ISBN 9781617299469
  • 352 pages
  • printed in black & white

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Look inside
Practical methods for analyzing your data with graphs, revealing hidden connections and new insights.

Graphs are the natural way to represent and understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with concrete advice on implementation and deployment. 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 makes them easy to apply to your own projects.

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.

about the technology

A graph, put simply, is a network of connected data. Graphs are an efficient way to identify and explore the significant relationships naturally occurring within a dataset. This book presents the most important algorithms for graph data science with examples from machine learning, business applications, natural language processing, and more.

about the book

Graph Algorithms for Data Science shows you how to construct and analyze graphs from structured and unstructured data. In it, you’ll learn to apply graph algorithms like PageRank, community detection/clustering, and knowledge graph models by putting each new algorithm to work in a hands-on data project. This cutting-edge book also demonstrates how you can create graphs that optimize input for AI models using node embedding.

what's inside

  • Creating knowledge graphs
  • Node classification and link prediction workflows
  • NLP techniques for graph construction

about the reader

For data scientists who know machine learning basics. Examples use the Cypher query language, which is explained in the book.

about the author

Tomaž Bratanič works at the intersection of graphs and machine learning.

Arturo Geigel was the technical editor for this book.

Undoubtedly the quickest route to grasping the practical applications of graph algorithms. Enjoyable and informative, with real-world business context and practical problem-solving.

Roger Yu, Feedzai

Brilliantly eases you into graph-based applications.

Sumit Pal, Independent Consultant

I highly recommend this book to anyone involved in analyzing large network databases.

Ivan Herreros, talentsconnect

Insightful and comprehensive. The author’s expertise is evident. Be prepared for a rewarding journey.

Michal Štefaňák, Volke

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team

monthly
annual
$49.99
$499.99
only $41.67 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose another free product every time you renew
  • choose twelve free products per year
  • exclusive 50% discount on all purchases
  • Graph Algorithms for Data Science ebook for free