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Reseña del editor
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.
Biografía del 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.
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- EditorialSpringer-Verlag GmbH
- Año de publicación2018
- ISBN 10 3319967452
- ISBN 13 9783319967455
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de edición1
- Número de páginas364
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Practical Social Network Analysis with Python (Computer Communications and Networks)
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Springer, 2018
ISBN 10: 3319967452
ISBN 13: 9783319967455
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Practical Social Network Analysis with Python
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Springer International Publishing Sep 2018, 2018
ISBN 10: 3319967452
ISBN 13: 9783319967455
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 364 pp. Englisch. Nº de ref. del artículo: 9783319967455
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Practical Social Network Analysis with Python
Publicado por
Springer International Publishing, 2018
ISBN 10: 3319967452
ISBN 13: 9783319967455
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783319967455
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Practical Social Network Analysis with Python
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ISBN 10: 3319967452
ISBN 13: 9783319967455
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces the fundamentals of social network analysisDiscusses key concepts and important analysis techniquesHighlights, with real-world examples, how large networks can be analyzed using deep learning techniquesDr. Krishna R. Nº de ref. del artículo: 231141327
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