Graph Algorithms
Practical Examples in Apache Spark and Neo4j
Published on 31. May 2019
Book
Paperback/Softback
300 pages
978-1-4920-4768-1 (ISBN)
Description
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
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
More details
Language
English
Place of publication
Sebastopol
United States
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 237 mm
Width: 177 mm
Thickness: 17 mm
Weight
446 gr
ISBN-13
978-1-4920-4768-1 (9781492047681)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
05/2019
O'Reilly
€58.99
Available for download
E-Book
05/2019
O'Reilly
€58.99
Available for download
Persons
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.
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.
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.