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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 carefullyselected papers on the subject, Towards an Information Theory of Complex Networks: Statistical Methods and Applications presents theoretical and practical results about informationtheoretic 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 graphtheoretic, informationtheoretic, and statistical methods as a way to better understand and characterize realworld networks.This volume is the first to present a selfcontained, comprehensive overview of informationtheoretic 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.
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