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.
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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 importance through centrality, community detection and optimal pathfinding. Read this book to:
- 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 to find out how subtle parameters impact results
- Get guidance on which algorithms to use for different types of questions
- Explore algorithm examples with working code and sample datasets for both Apache 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 Apache Spark
Discover how graph algorithms help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models.— Graph Algorithms: Practical Examples in Apache Spark and Neo4j