Graph Algorithms: Practical Examples in Apache Spark and Neo4j
Language: English
Published by O'Reilly Media, 2019
- Softcover
- Used
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Used - Acceptable: All pages and the cover are intact, but shrink wrap, dust covers, or boxed set case may be missing. Pages may include limited notes, highlighting, or minor water damage but the text is readable. Item may be missing bundled media.
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- Title
- Graph Algorithms: Practical Examples in Apache Spark and Neo4j
- Author
- Needham, Mark; Hodler, Amy E.
- Publisher
- O'Reilly Media
- Publication year
- 2019
- Condition
- acceptable
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1492047686
- ISBN 13
- 9781492047681
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
"Synopsis" may belong to another edition of this title.
About the Author
Amy Hodler is a network science devotee and AI and Graph Analytics Program Manager at Neo4j. She promotes the use of graph analytics to reveal structures within real-world networks and predict dynamic behavior. Amy helps teams apply novel approaches to generate new opportunities at companies such as EDS, Microsoft, Hewlett-Packard (HP), Hitachi IoT, and Cray Inc. Amy has a love for science and art with a fascination for complexity studies and graph theory.
"About the title" may belong to another edition of this title.
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