Название: Social Networks: Modelling and Analysis
Автор: Niyati Aggrawal, Adarsh Anand
Издательство: CRC Press
Год: 2022
Страниц: 254
Язык: английский
Формат: pdf (true)
Размер: 12.8 MB

The goal of this book is to provide a reference for applications of mathematical modeling in social media and related network analysis and offer a theoretically sound background with adequate suggestions for better decision-making.

Social Networks: Modeling and Analysis provide the essential knowledge of network analysis applicable to real-world data, with examples from today's most popular social networks such as Facebook, Twitter, Instagram, YouTube, etc. The book provides basic notation and terminology used in social media and its network science. It covers the analysis of statistics for social network analysis such as degree distribution, centrality, clustering coefficient, diameter, and path length. The ranking of the pages using rank algorithms like Page Rank and HITS are also discussed.

The topics covered are organized as follows:
Chapter 1 discusses the basic concepts and terminologies related to network analysis applicable to real-world data, with examples from today's most popular social networks. Chapter 2 discusses the need for scientific analysis wherein the representation of a network as a graph (various kind of graphs) and learnings pertaining to distinguishing weighted, directed and bipartite networks have been discussed. In Chapter 3, various networking models such as the random graph model, small-world model and scale-free model have been discussed. Centrality measures attempt to find the most central node within a graph. In Chapter 4, various centrality measures for a social media network have been explained. Chapter 5 associates a relative quantitative assessment with each entity of the network using link-based measures such as PageRank and HITS whereas in Chapter 6, the approaches to link prediction based on measures for analyzing the ‘proximity’ of vertices in a network have been worked upon. In Chapter 7, various community detection techniques such as node-centric community detection (cliques, k-cliques, k-clubs) and group-centric community detection have been discussed in detail. Examining the ego networks of individuals can provide insight into why one individual's perceptions, identity and behaviour differ from another's. In Chapter 8, the focus has been on the individual actor, rather than the network as a whole. The fundamental idea here is the different ways in which individuals are attached to macro-structures. In Chapter 9, the idea of the amount of ‘embedding’ in whole networks, the extent to which actors find themselves in social structures characterized by dense, reciprocal, transitive and strong ties, has been presented in detail. Chapter 10 gives an overview of the information diffusion process on social networking platforms and various mathematical modelling frameworks. Chapter 11 gives a brief overview about security and privacy issues on social networks, and Chapter 12 provides an overview of the various social networking tools such as Gephi, UCINET, NetworkX, Pajek and many more.

Written as a reference this book is for Engineering and Management Students, Research Scientists, Academicians involved in complex networks, mathematical sciences, and marketing research.




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