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The proliferation of social media such as real time microblogging
and online reputation systems facilitate real time sensing of
social patterns and behavior. In the last decade, sensing and
decision making in social networks have witnessed significant
progress in the electrical engineering, computer science,
economics, finance, and sociology research communities. Research in
this area involves the interaction of dynamic random graphs,
socio-economic analysis, and statistical inference algorithms.
Interactive Sensing and Decision Making in Social Networks provides
a survey, tutorial development, and discussion of four highly
stylized examples of sensing and decision making in social
networks: social learning for interactive sensing; tracking the
degree distribution of social networks; sensing and information
diffusion; and coordination of decision making via game-theoretic
learning. Each of the four examples is motivated by practical
examples, and comprises of a literature survey together with
careful problem formulation and mathematical analysis. Despite
being highly stylized, these examples provide a rich variety of
models, algorithms and analysis tools that are readily accessible
to a signal processing, control/systems theory, and applied
mathematics audience.
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