Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. 

You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. 

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

This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. 

Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection.

• 📊 Learn the key differences between graph analytics and conventional statistical analysis.
• 🔄 Understand the workings of classic graph algorithms and their practical applications.
• 🤔 Get guidance on selecting the appropriate algorithms for various types of analytical questions.
• 🖥️ Explore algorithm examples with implemented code and sample datasets from Spark and Neo4j.
• 🌐 See how connected feature extraction enhances machine learning accuracy and precision.
• 🛠️ Walk through the process of creating a machine learning workflow for link prediction using Neo4j and Spark.