000 04419nam a22005535i 4500
001 978-0-8176-4904-3
003 DE-He213
005 20140220083711.0
007 cr nn 008mamaa
008 110824s2011 xxu| s |||| 0|eng d
020 _a9780817649043
_9978-0-8176-4904-3
024 7 _a10.1007/978-0-8176-4904-3
_2doi
050 4 _aQ350-390
050 4 _aQA10.4
072 7 _aPBW
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
100 1 _aDehmer, Matthias.
_eeditor.
245 1 0 _aTowards an Information Theory of Complex Networks
_h[electronic resource] :
_bStatistical Methods and Applications /
_cedited by Matthias Dehmer, Frank Emmert-Streib, Alexander Mehler.
250 _a1.
264 1 _aBoston :
_bBirkhäuser Boston,
_c2011.
300 _aXVI, 395p. 114 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPreface -- Entropy of Digraphs and Infinite Networks -- An Information-Theoretic Upper Bound on Planar Graphs Using Well-orderly Maps -- Probabilistic Inference Using Function Factorization and Divergence Minimization -- Wave Localization on Complex Networks -- Information-Theoretic Methods in Chemical Graph Theory -- On the Development and Application of Net-Sign Graph Theory -- The Central Role of Information Theory in Ecology -- Inferences About Coupling from Ecological Surveillance Monitoring -- Markov Entropy Centrality -- Social Ontologies as Generalizedd Nearly Acyclic Directed Graphs -- Typology by Means of Language Networks -- Information Theory-Based Measurement of Software -- Fair and Biased Random Walks on Undirected Graphs and Related Entropies.
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. 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 theory ecosystem interaction dynamics social ontologies language networks software 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.
650 0 _aMathematics.
650 0 _aCoding theory.
650 0 _aArtificial intelligence.
650 0 _aPhysiology
_xMathematics.
650 0 _aTelecommunication.
650 1 4 _aMathematics.
650 2 4 _aInformation and Communication, Circuits.
650 2 4 _aCoding and Information Theory.
650 2 4 _aPhysiological, Cellular and Medical Topics.
650 2 4 _aCommunications Engineering, Networks.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aApplications of Mathematics.
700 1 _aEmmert-Streib, Frank.
_eeditor.
700 1 _aMehler, Alexander.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780817649036
856 4 0 _uhttp://dx.doi.org/10.1007/978-0-8176-4904-3
912 _aZDB-2-SMA
999 _c105086
_d105086