Focused on the mathematical foundations of social media analysis,
Graph-Based Social Media Analysis provides a comprehensive
introduction to the use of graph analysis in the study of social
and digital media. It addresses an important scientific and
technological challenge, namely the confluence of graph analysis
and network theory with linear algebra, digital media, machine
learning, big data analysis, and signal processing. Supplying an
overview of graph-based social media analysis, the book provides
readers with a clear understanding of social media structure. It
uses graph theory, particularly the algebraic description and
analysis of graphs, in social media studies. The book emphasizes
the big data aspects of social and digital media. It presents
various approaches to storing vast amounts of data online and
retrieving that data in real-time. It demystifies complex social
media phenomena, such as information diffusion, marketing and
recommendation systems in social media, and evolving systems. It
also covers emerging trends, such as big data analysis and social
media evolution. Describing how to conduct proper analysis of the
social and digital media markets, the book provides insights into
processing, storing, and visualizing big social media data and
social graphs. It includes coverage of graphs in social and digital
media, graph and hyper-graph fundamentals, mathematical foundations
coming from linear algebra, algebraic graph analysis, graph
clustering, community detection, graph matching, web search based
on ranking, label propagation and diffusion in social media,
graph-based pattern recognition and machine learning, graph-based
pattern classification and dimensionality reduction, and much more.
This book is an ideal reference for scientists and engineers
working in social media and digital media production and
distribution. It is also suitable for use as a textbook in
undergraduate or graduate courses on digital media, social media,
or social networks.
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