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

We walk you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j. We include sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection using methods like clustering and partitioning. Read this book to:
Download Your Free Copy of O'Reilly’s Graph Algorithms

Learn how graph analytics vary from conventional statistical analysis
Understand how classic graph algorithms work and how they are applied
Dive into popular algorithms like PageRank, Label Propagation and Louvain Modularity to find out how subtle parameters impact results
Get guidance on which algorithms to use for different types of questions
Explore graph algorithm examples with working code and sample datasets for both Spark and Neo4j
See how connected feature extraction increases machine learning accuracy and precision
Walk through creating an ML workflow for link prediction combining Neo4j and Spark

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