Practical Social Network Analysis with Python

Krishna Raj P. M.

ISBN 10: 3319967452 / ISBN 13: 9783319967455

Verlag: Springer International Publishing, Springer International Publishing Sep 2018, 2018

Beschreibung

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 364 pp. Englisch.

Bestandsnummer des Verkäufers 9783319967455

Bibliografische Details

• Titel: Practical Social Network Analysis with Python
• Verlag: Springer International Publishing, Springer International Publishing Sep 2018
• Veröffentlichungsdatum: 2018
• Sprache: Englisch
• ISBN-10: 3319967452
• ISBN-13: 9783319967455
• Bindung: Buch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

Dr. Krishna Raj P.M. is an Associate Professor at the Department of Information Science and Engineering at Ramaiah Institute of Technology, Bengaluru, India.

Mr. Ankith Mohan is a Research Associate at the same institution.

Dr. Srinivasa K.G. is an Associate Professor at the Department of Information Technology at Ch. Brahm Prakash Government Engineering College, Delhi, India.