Summary

Whether you are building dynamic network models or forecasting real-world behavior, this book illustrates how graph algorithms deliver value: from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions.

We walk you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j. We include sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection using methods like clustering and partitioning.

NEW: The Neo4j Graph Data Science (GDS) Library, available here, is the successor to the former Graph Algorithms Library. This book has been updated to reflect examples from the new GDS library. The minor syntax changes are covered in the migration guide and this post wallks through converting examples from the deprecated Graph Algorithms library.

Read this book to: