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Practical Data Analysis

You're reading from  Practical Data Analysis

Product type Book
Published in Oct 2013
Publisher Packt
ISBN-13 9781783280995
Pages 360 pages
Edition 1st Edition
Languages
Author (1):
Hector Cuesta Hector Cuesta
Profile icon Hector Cuesta
Toc

Table of Contents (24) Chapters close

Practical Data Analysis
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started 2. Working with Data 3. Data Visualization 4. Text Classification 5. Similarity-based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Disease with Cellular Automata 10. Working with Social Graphs 11. Sentiment Analysis of Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with IPython and Wakari Setting Up the Infrastructure Index

Structure of a graph


A graph is a set of nodes (or vertices) and links (or edges). Each link is a pair of node references (such as source or target). Links may be considered as directed or undirected, depending if the relationship is mutual or not. The most common way to computationally represent a graph is by using an adjacency matrix. We use the index of the matrix as a node identifier and the value of the coordinates to represent whether there exists a link (the value is 1) or not (the value is 0). The links between nodes may have a scalar value (weight) to define a distance between the nodes. Graphs are widely used in Sociology, Epidemiology, Internet, Government, Commerce, and Social networks to find groups and information diffusion.

Graph analytics can be split into three categories:

  • Structural algorithms

  • Traversal algorithms

  • Pattern-matching algorithms

Undirected graph

In the undirected graph, there is no distinction between the nodes source and target. As we can observe in the following...

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