Graph Algorithms: Practical Examples in Apache Spark and Neo4j

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 dataUnderstand how popular graph algorithms work and how they're appliedUse sample code and tips from more than 20 graph algorithm examplesLearn which algorithms to use for different types of questionsExplore examples with working code and sample datasets for Spark and Neo4jCreate an ML workflow for link prediction by combining Neo4j and Spark