Graph Algorithms for Data Science [Audiobook]

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.