Graph Algorithms: Practical Examples in Apache Spark and Neo4j

October 31, 2020
Graph Algorithms: Practical Examples in Apache Spark and Neo4j

Discover how graph algorithms can help you leverage therelationships within your data to develop more intelligentsolutions and enhance your machine learning models. You'll learnhow graph analytics are uniquely suited to unfold complexstructures and reveal difficult-to-find patterns lurking in yourdata. Whether you are trying to build dynamic network models orforecast real-world behavior, this book illustrates how graphalgorithms deliver value—from finding vulnerabilities andbottlenecks to detecting communities and improving machine learningpredictions.This practical book walks you through hands-on examples of howto use graph algorithms in Apache Spark and Neo4j—two of the mostcommon choices for graph analytics. Also included: sample code andtips for over 20 practical graph algorithms that cover optimalpathfinding, importance through centrality, and communitydetection.Learn how graph analytics vary from conventional statisticalanalysisUnderstand how classic graph algorithms work, and how they areappliedGet guidance on which algorithms to use for different types ofquestionsExplore algorithm examples with working code and sampledatasets from Spark and Neo4jSee how connected feature extraction can increase machinelearning accuracy and precisionWalk through creating an ML workflow for link predictioncombining Neo4j and Spark