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Graph algorithms
Paperback
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- Book Synopsis
- Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide, developers and data scientists will discover how graph analytics deliver value, whether they're used for building dynamic network models or forecasting real-world behavior. Mark Needham and Amy Hodler from Neo4j explain how graph algorithms describe complex structures and reveal difficult-to-find patterns-from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. You'll walk through hands-on examples that show you how to use graph algorithms in Apache Spark and Neo4j, two of the most common choices for graph analytics. Learn how graph analytics reveal more predictive elements in today's data Understand how popular graph algorithms work and how they're applied Use sample code and tips from more than 20 graph algorithm examples Learn which algorithms to use for different types of questions Explore examples with working code and sample datasets for Spark and Neo4j Create an ML workflow for link prediction by combining Neo4j and Spark
- About The Author
- Mark Needham is a graph advocate and Developer Relations Engineer at Neo4j. Mark helps users embrace graphs and Neo4j, building sophisticated solutions to challenging data problems. Mark has deep expertise in graph data having previously helped to build Neo4j's Causal Clustering system. Mark writes about his experiences of being a graphista on a popular blog at markhneedham.com.
- Product Details
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- ISBN
- 9781492047681
- Format
- Paperback
- Publisher
- O'Reilly, (31 May 2019)
- Number of Pages
- 265
- Weight
- 444 grams
- Language
- English
- Dimensions
- 233 x 178 x 14 mm
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