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Apache Spark 2.x Cookbook

You're reading from  Apache Spark 2.x Cookbook

Product type Book
Published in May 2017
Publisher
ISBN-13 9781787127265
Pages 294 pages
Edition 1st Edition
Languages
Author (1):
Rishi Yadav Rishi Yadav
Profile icon Rishi Yadav
Toc

Table of Contents (19) Chapters close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Apache Spark 2. Developing Applications with Spark 3. Spark SQL 4. Working with External Data Sources 5. Spark Streaming 6. Getting Started with Machine Learning 7. Supervised Learning with MLlib — Regression 8. Supervised Learning with MLlib — Classification 9. Unsupervised Learning 10. Recommendations Using Collaborative Filtering 11. Graph Processing Using GraphX and GraphFrames 12. Optimizations and Performance Tuning

Finding connected components


A connected component is a subgraph (a graph whose vertices are a subset of the vertex set of the original graph and whose edges are a subset of the edge set of the original graph) in which any two vertices are connected to each other by an edge or a series of edges.

An easy way to understand it would be by taking a look at the road network graph of Hawaii. This state has numerous islands, which are not connected by roads. Within each island, most roads will be connected to each other. The goal of finding the connected components is to find these clusters.

The connected components algorithm labels each connected component of the graph with the ID of its lowest-numbered vertex.

Getting ready

We will build a small graph here for the clusters we know and use connected components to segregate them. Let's look at the following data:

Follower

Followee

John

Pat

Pat

Dave

Gary

Chris

Chris

Bill

The preceding data is a simple one, with six vertices and two clusters. Let's put this data...

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