**by Mark Needham, Amy Hodler**

DescriptionTable of ContentsDetailsHashtagsReport an issue ### 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 ### Table of Contents

### Book Details

### Related Books

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).

Chapter 1

Introduction

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

Index

Title

Graph Algorithms

Subject

Computer Science

Publisher

O'Reilly Media

Published

2019

Pages

257

Edition

1

Language

English

ISBN13

9781492047681

ISBN10

1492047686

ISBN13 Digital

9781492057819

ISBN10 Digital

1492057819

PDF Size

10.8 MB

License

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