Practical Social Network Analysis with Python (Computer Communications and Networks)

- 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 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. Front Matter ....Pages i-xxxi Basics of Graph Theory (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 1-23 Graph Structure of the Web (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 25-44 Random Graph Models (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 45-56 Small World Phenomena (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 57-85 Graph Structure of Facebook (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 87-100 Peer-To-Peer Networks (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 101-108 Signed Networks (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 109-144 Cascading in Social Networks (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 145-172 Influence Maximisation (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 173-188 Outbreak Detection (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 189-202 Power Law (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 203-232 Kronecker Graphs (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 233-243 Link Analysis (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 245-278 Community Detection (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 279-299 Representation Learning on Graphs (Krishna Raj P. M., Ankith Mohan, K. G. Srinivasa)....Pages 301-317 Back Matter ....Pages 319-329