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020			_a9780817649036
037			_cTextbook
040			_aCSL _beng _cCSL
041			_aeng
084			_aC:(D65,8(B):71) Q1 TC _qCSL
100			_aDehmer, Mathias _91116881
245		0	_aTowards an information theory of complex networks: statistical methods and applications
260			_aNew York: _bBirkhauser, _c2011.
300			_axvi, 395p. _b: ill.
500			_aIncluding bibliographical reference
520			_aFor 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.
650			_aCompex networks _91116882
650			_aMarkov entropy centrality _91116883
650			_aInformation theory _91116884
700			_aDehmer, Mathias _91116881
700			_aEmmert-Streib, Frank _9809297
700			_aMehler, Alexander _91116885
942			_hC:(D65,8(B):71) Q1 TC _cTB _2CC
999			_c13687 _d13687