Towards an Information Theory of Complex Networks

For over a decade, complex networks have steadily grown as an important tool across a#xA0;broad array of academic disciplines, with applications ranging from physics to social media.#xA0;A#xA0; tightly organized collection#xA0;of#xA0;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.#xA0;The book's#xA0;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#xA0;of complex networks with an emphasis on applications. As such, it#xA0;marks a first step toward establishing advanced statistical information theory as a unified theoretical basis of complex networks#xA0;for all#xA0;scientific disciplines and#xA0;can serve as#xA0;a valuable resource for#xA0;a#xA0;diverse audience of advanced students and professional scientists.#xA0;While it is primarily#xA0;intended#xA0;as a reference for research,#xA0;the book#xA0;could also be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others.