Graph Algorithms

Practical Examples in Apache Spark and Neo4j

by Mark Needham, Amy Hodler

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Book 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

This open book is licensed under a Creative Commons License (CC BY). You can download Graph Algorithms ebook for free in PDF format (10.8 MB).

Table of Contents

Chapter 1
Chapter 2
Graph Theory and Concepts
Chapter 3
Graph Platforms and Processing
Chapter 4
Pathfinding and Graph Search Algorithms
Chapter 5
Centrality Algorithms
Chapter 6
Community Detection Algorithms
Chapter 7
Graph Algorithms in Practice
Chapter 8
Using Graph Algorithms to Enhance Machine Learning
Appendix A
Additional Information and Resources

Book Details

Computer Science
O'Reilly Media
ISBN13 Digital
ISBN10 Digital
PDF Size
10.8 MB

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