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Practical social network analysis wi...

M., Krishna Raj P.

Practical social network analysis with Python

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Practical social network analysis with Pythonby Krishna Raj P.M., Ankith Mohan, K.G. Srinivasa.
作者:	M., Krishna Raj P.
其他作者:	Mohan, Ankith.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xxxi, 329 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Online social networks.
電子資源:	http://dx.doi.org/10.1007/978-3-319-96746-2
ISBN:	9783319967462$q(electronic bk.)

Practical social network analysis with Python
M., Krishna Raj P.

Practical social network analysis with Python[electronic resource] /by Krishna Raj P.M., Ankith Mohan, K.G. Srinivasa. - Cham :Springer International Publishing :2018. - xxxi, 329 p. :ill., digital ;24 cm. - Computer communications and networks,1617-7975. - Computer communications and networks..

Chapter 1. Basics of Graph Theory -- Chapter 2. Graph Structure of the Web -- Chapter 3. Random Graph Models -- Chapter 4. Small World Phenomena -- Chapter 5. Graph Structure of Facebook -- Chapter 6. Peer-To-Peer Networks -- Chapter 7. Signed Networks -- Chapter 8. Cascading in Social Networks -- Chapter 9. Inﬂuence Maximisation -- Chapter 10. Outbreak Detection -- Chapter 11. Power Law -- Chapter 12. Kronecker Graphs -- Chapter 13. Link Analysis -- Chapter 14. Community Detection -- Chapter 15. Representation Learning on Graph.

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.

ISBN: 9783319967462$q(electronic bk.)

Standard No.: 10.1007/978-3-319-96746-2doiSubjects--Topical Terms:

281852
Online social networks.

LC Class. No.: HM742

Dewey Class. No.: 006.754

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505 0 $a Chapter 1. Basics of Graph Theory -- Chapter 2. Graph Structure of the Web -- Chapter 3. Random Graph Models -- Chapter 4. Small World Phenomena -- Chapter 5. Graph Structure of Facebook -- Chapter 6. Peer-To-Peer Networks -- Chapter 7. Signed Networks -- Chapter 8. Cascading in Social Networks -- Chapter 9. Inﬂuence Maximisation -- Chapter 10. Outbreak Detection -- Chapter 11. Power Law -- Chapter 12. Kronecker Graphs -- Chapter 13. Link Analysis -- Chapter 14. Community Detection -- Chapter 15. Representation Learning on Graph.
520 $a 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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