Practical Social Network Analysis with Python

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Éditeur :

Springer


Collection :

Computer Communications and Networks

Paru le : 2018-08-25

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Description

This book focuses on social network analysis from a computational perspective, introducing readers to the fundamental aspects of network theory by discussing the various metrics used to measure the social network. It covers different forms of graphs and their analysis using techniques like filtering, clustering and rule mining, as well as important theories like small world phenomenon. It also presents methods for identifying influential nodes in the network and information dissemination models. Further, it uses examples to explain the tools for visualising large-scale networks, and explores emerging topics like big data and deep learning in the context of social network analysis.
With the Internet becoming part of our everyday lives, social networking tools are used as the primary means of communication. And as the volume and speed of such data is increasing rapidly, there is a need to apply computational techniques to interpret and understand it. Moreover, relationships in molecular structures, co-authors in scientific journals, and developers in a software community can also be understood better by visualising them as networks.

This book brings together the theory and practice of social network analysis and includes mathematical concepts, computational techniques and examples from the real world to offer readers an overview of this domain.


Pages
329 pages
Collection
Computer Communications and Networks
Parution
2018-08-25
Marque
Springer
EAN papier
9783319967455
EAN PDF
9783319967462

Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
32
Taille du fichier
15544 Ko
Prix
94,94 €
EAN EPUB
9783319967462

Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
32
Taille du fichier
11500 Ko
Prix
94,94 €