Introduces the fundamentals of social network analysis
Discusses key concepts and important analysis techniques
Highlights, with real-world examples, how large networks can be analyzed using deep learning techniques
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.
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.
Introduction
Basics of Social Networks
Random Graphs and Small World Phenomenon
Analysis of Social Networks
Centrality and Influential Nodes
Information Propagation in Social Networks
Visual Modelling of Social Networks
Processing Large Scale Social Networks
Deep Learning for Social Networks
Applications of Social Network Analysis
Appendix: IPython Tutorials
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.
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. In uence 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.
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.
Über den Autor
rn Dr. Krishna Raj P.M. is an Associate Professor at the Department of Information Science and Engineering at Ramaiah Institute of Technology, Bengaluru, India.
rn
rn Mr. Ankith Mohan is a Research Associate at the same institution.
rn
rn
rn Dr. Srinivasa K.G. is an Associate Professor at the Department of Information Technology at Ch. Brahm Prakash Government Engineering College, Delhi, India.
rn
rn
Inhaltsverzeichnis
Introduction
Basics of Social Networks
Random Graphs and Small World Phenomenon
Analysis of Social Networks
Centrality and Influential Nodes
Information Propagation in Social Networks
Visual Modelling of Social Networks
Processing Large Scale Social Networks
Deep Learning for Social Networks
Applications of Social Network Analysis
Appendix: IPython Tutorials
Klappentext
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.rnWith 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.rnrnThis 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.rn
rn
Introduces the fundamentals of social network analysis
Discusses key concepts and important analysis techniques
Highlights, with real-world examples, how large networks can be analyzed using deep learning techniques
