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Autor(en): 
  • K.G. Srinivasa
  • Ankith Mohan
  • Krishna Raj P.M.
  • Practical Social Network Analysis with Python 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  September 2018  
    Genre:  EDV / Informatik 
     
    B / Computer Communication Networks / Computer communication systems / computer science / Programmier- und Skriptsprachen, allgemein / Programming & scripting languages# general / python / Python (Computer program language)
    ISBN:  9783319967455 
    EAN-Code: 
    9783319967455 
    Verlag:  Springer EN 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  Computer Communications and Networks  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  863 gr 
    Seiten:  329 
    Illustration:  XXXI, 329 p. 186 illus., 73 illus. in color. With online files/update., schwarz-weiss Illustrationen, farbige Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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.


      



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