Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. Youll 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 valuefrom 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 Neo4jtwo 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 how graph analytics vary from conventional statistical analysisUnderstand how classic graph algorithms work, and how they are appliedGet guidance on which algorithms to use for different types of questionsExplore algorithm examples with working code and sample datasets from Spark and Neo4jSee how connected feature extraction can increase machine learning accuracy and precisionWalk through creating an ML workflow for link prediction combining Neo4j and Spark

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. Amy helps teams apply novel approaches to generate new opportunities at companies such as EDS, Microsoft, Hewlett-Packard (HP), Hitachi IoT, and Cray Inc. Amy has a love for science and art with a fascination for complexity studies and graph theory.