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R Data Analysis Projects

You're reading from   R Data Analysis Projects Build end to end analytics systems to get deeper insights from your data

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788621878
Length 366 pages
Edition 1st Edition
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Author (1):
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Gopi Subramanian Gopi Subramanian
Author Profile Icon Gopi Subramanian
Gopi Subramanian
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Table of Contents (15) Chapters Close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Association Rule Mining FREE CHAPTER 2. Fuzzy Logic Induced Content-Based Recommendation 3. Collaborative Filtering 4. Taming Time Series Data Using Deep Neural Networks 5. Twitter Text Sentiment Classification Using Kernel Density Estimates 6. Record Linkage - Stochastic and Machine Learning Approaches 7. Streaming Data Clustering Analysis in R 8. Analyze and Understand Networks Using R

Chapter 8. Analyze and Understand Networks Using R

Network analysis is the study of graphs. Graphs are defined by a set of nodes or vertices connected by edges. Both the nodes and vertices can have attributes describing them. Most importantly, the edges can carry weight, indicating the importance of the connection. When the directions of the edges are preserved, the graph is called a directed graph; when not preserved, it's called an undirected graph. Network analysis, or network theory, or graph theory provides a rich set of algorithms to analyze and understand graphs. The famous Koenigsberg problem (http://mathworld.wolfram.com/KoenigsbergBridgeProblem.html) introduced by Euler is one of the first graph theory problems to be studied. Koenigsberg is an old city in Prussia (modern day Russia). The river Pregal separates the city. There are two other islands. There are seven bridges connecting the islands and the cities. The Koenigsberg problem was to devise a walk through the city that would...

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