Graph Algorithms: Practical Examples in Apache Spark and Neo4j 1st Edition

Learn how charting algorithms can help you leverage the relationships within your data to develop smarter solutions and improve your machine learning models. You will learn how chart analysis is particularly well suited for visualizing complex structures and revealing hard-to-find patterns lurking in your data. Whether you're looking to create dynamic network models or predict behavior in the real world, this book illustrates how graphing algorithms deliver value, from finding vulnerabilities and bottlenecks to discovering communities and improving learning predictions.
This hands-on book walks you through hands-on examples on how to use graph algorithms in Apache Spark and Neo4j, two of the most common options for graph analysis. Also included: sample code and suggestions for over 20 hands-on graphical algorithms that cover finding the optimal path, importance through centrality and community discovery.

1. Learn how graph analytics vary from conventional statistical analysis
2. Understand how classic graph algorithms work, and how they are applied
3. Get guidance on which algorithms to use for different types of questions
4. Explore algorithm examples with working code and sample datasets from Spark and Neo4j
5. See how connected feature extraction can increase machine learning accuracy and precision
6. Walk through creating an ML workflow for link prediction combining Neo4j and Spark

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Language:English
Authors : Mark Needham, Amy E. Hodler