Graph Algorithms

Download free course Graph Algorithms, pdf file on 257 pages by Mark Needham, Amy Hodler.

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

Introduction
Graph Theory and Concepts
Graph Platforms and Processing
Pathfinding and Graph Search Algorithms
Centrality Algorithms
Community Detection Algorithms
Graph Algorithms in Practice
Using Graph Algorithms to Enhance Machine Learning
Additional Information and Resources

Pages :	257
Size :	10.8 MB
File type :	PDF
Downloads:	64
Created:	2022-02-02
License:	CC BY
Author(s):	Mark Needham, Amy Hodler

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