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Python for graph and network analysis

Al-Taie, Mohammed Zuhair.

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Python for graph and network analysis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Python for graph and network analysis/ by Mohammed Zuhair Al-Taie, Seifedine Kadry.
Author:	Al-Taie, Mohammed Zuhair.
other author:	Kadry, Seifedine.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xiii, 203 p. :ill., digital ;24 cm.
[NT 15003449]:	Theoretical Concepts of Network Analysis -- Network Basics -- Graph Theory -- Social Networks -- Node-Level Analysis -- Group-Level Analysis -- Network-Level Analysis -- Information Diffusion in Social Networks -- Appendix A: Python Tutorial -- Appendix B: NetworkX Tutorial.
Contained By:	Springer eBooks
Subject:	Python (Computer program language) -
Online resource:	http://dx.doi.org/10.1007/978-3-319-53004-8
ISBN:	9783319530048

Python for graph and network analysis
Al-Taie, Mohammed Zuhair.

Python for graph and network analysis[electronic resource] /by Mohammed Zuhair Al-Taie, Seifedine Kadry. - Cham :Springer International Publishing :2017. - xiii, 203 p. :ill., digital ;24 cm. - Advanced information and knowledge processing,1610-3947. - Advanced information and knowledge processing..

Theoretical Concepts of Network Analysis -- Network Basics -- Graph Theory -- Social Networks -- Node-Level Analysis -- Group-Level Analysis -- Network-Level Analysis -- Information Diffusion in Social Networks -- Appendix A: Python Tutorial -- Appendix B: NetworkX Tutorial.

This research monograph provides the means to learn the theory and practice of graph and network analysis using the Python programming language. The social network analysis techniques, included, will help readers to efficiently analyze social data from Twitter, Facebook, LiveJournal, GitHub and many others at three levels of depth: ego, group, and community. They will be able to analyse militant and revolutionary networks and candidate networks during elections. For instance, they will learn how the Ebola virus spread through communities. Practically, the book is suitable for courses on social network analysis in all disciplines that use social methodology. In the study of social networks, social network analysis makes an interesting interdisciplinary research area, where computer scientists and sociologists bring their competence to a level that will enable them to meet the challenges of this fast-developing field. Computer scientists have the knowledge to parse and process data while sociologists have the experience that is required for efficient data editing and interpretation. Social network analysis has successfully been applied in different fields such as health, cyber security, business, animal social networks, information retrieval, and communications.

ISBN: 9783319530048

Standard No.: 10.1007/978-3-319-53004-8doiSubjects--Topical Terms:

729789
Python (Computer program language)

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.133

Python for graph and network analysis
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100 1 $a Al-Taie, Mohammed Zuhair. $3 3226541
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260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xiii, 203 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advanced information and knowledge processing, $x 1610-3947
505 0 $a Theoretical Concepts of Network Analysis -- Network Basics -- Graph Theory -- Social Networks -- Node-Level Analysis -- Group-Level Analysis -- Network-Level Analysis -- Information Diffusion in Social Networks -- Appendix A: Python Tutorial -- Appendix B: NetworkX Tutorial.
520 $a This research monograph provides the means to learn the theory and practice of graph and network analysis using the Python programming language. The social network analysis techniques, included, will help readers to efficiently analyze social data from Twitter, Facebook, LiveJournal, GitHub and many others at three levels of depth: ego, group, and community. They will be able to analyse militant and revolutionary networks and candidate networks during elections. For instance, they will learn how the Ebola virus spread through communities. Practically, the book is suitable for courses on social network analysis in all disciplines that use social methodology. In the study of social networks, social network analysis makes an interesting interdisciplinary research area, where computer scientists and sociologists bring their competence to a level that will enable them to meet the challenges of this fast-developing field. Computer scientists have the knowledge to parse and process data while sociologists have the experience that is required for efficient data editing and interpretation. Social network analysis has successfully been applied in different fields such as health, cyber security, business, animal social networks, information retrieval, and communications.
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