| Title | : | Towards an Information Theory of Complex Networks |
|---|---|---|
| Author | : | Matthias Dehmer, Frank Emmert-Streib & Alexander Mehler |
| Release | : | 2011-08-26 |
| Kind | : | ebook |
| Genre | : | Mathematics, Books, Science & Nature, Professional & Technical, Engineering, Computers & Internet, Computers |
| Size | : | 12190412 |
| 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. It begins with four chapters developing the most significant formal-theoretical issues of network modeling, but the majority of the book is devoted to combining theoretical results with an empirical analysis of real networks. Specific topics include: chemical graph theoryecosystem interaction dynamicssocial ontologieslanguage networkssoftware systems This work marks a first step toward establishing advanced statistical information theory as a unified theoretical basis of complex networks for all scientific disciplines. As such, it can serve as a valuable resource for a diverse audience of advanced students and professional scientists. It is primarily intended as a reference for research, but could also be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others. |
