Graph Algorithms: Practical Examples in Apache Spark and Neo4j

Graph Algorithms – Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide, developers and data scientists will discover how graph analytics deliver value, whether they’re used for building dynamic network models or forecasting real-world behavior.

This book is a practical guide to getting started with graph algorithms for developers and data scientists who have experience using Apache Spark or Neo4j. Although our algorithm examples utilize the Spark and Neo4j platforms, this book will also be helpful for understanding more general graph concepts, regardless of your choice of graph technologies.

It explains how graph algorithms describe complex structures and reveal difficult-to-find patterns-from finding vulnerabilities and bottlenecksto detecting communities and improving machine learning predictions. You’ll walk through hands-on examples that show you how to use graph algorithms in Apache Spark and Neo4j, two of the most common choices for graph analytics.

  • Learn how graph analytics reveal more predictive elements in today’s data
  • Understand how popular graph algorithms work and how they’re applied
  • Use sample code and tips from more than 20 graph algorithm examples
  • Learn which algorithms to use for different types of questions
  • Explore examples with working code and sample datasets for Spark and Neo4j
  • Create an ML workflow for link prediction by combining Neo4j and Spark


About the Authors

  • Mark Needham is a graph advocate and Developer Relations Engineer at Neo4j. Mark helps users embrace graphs and Neo4j, building sophisticated solutions to challenging data problems.
  • Amy Hodler is a network science devotee and AI and Graph Analytics Program Manager at Neo4j. She promotes the use of graph analytics to reveal structures within real-world networks and predict dynamic behavior.