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.
NEW: The Neo4j Graph Data Science (GDS) Library, available here, is the successor to the former Graph Algorithms Library. This book has been updated to reflect examples from the new GDS library. The minor syntax changes are covered in the migration guide and this post walks through converting examples from the deprecated Graph Algorithms library.
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 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
Fill out the form for your free copy of Graph Algorithms: Practical Examples in Apache Spark and Neo4j by Mark Needham and Amy E. Hodler.
