Towards an Information Theory of Complex Networks

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For over a decade, complex networks have steadily grown as an important tool across a¿broad array of academic disciplines, with applications ranging from physics to social media.¿A¿ tightly organized collection¿of¿carefully-selected papers on the subject, Towards an Information Theory of Complex Networks: Statistical Methods and Applications presents theoretical and practical results about information-theoretic and statistical models of complex networks in the natural sciences and humanities.¿The book's¿major goal is to advocate and promote a combination of graph-theoretic, information-theoretic, and statistical methods as a way to better understand and characterize real-world networks. This volume is the first to present a self-contained, comprehensive overview of information-theoretic models¿of complex networks with an emphasis on applications. As such, it¿marks a first step toward establishing advanced statistical information theory as a unified theoretical basis of complex networks¿for all¿scientific disciplines and¿can serve as¿a valuable resource for¿a¿diverse audience of advanced students and professional scientists.¿While it is primarily¿intended¿as a reference for research, ¿the book¿could also be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others.