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024 7 $a10.1007/b106453
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035   $a(PQKBTitleCode)TC0000319120
035   $a(PQKBWorkID)10337293
035   $a(PQKB)11740043
035   $a(DE-He213)978-3-540-31955-9
035   $a(MiAaPQ)EBC3067723
035   $a(PPN)123092191
035   $a(EXLCZ)991000000000212851
100   $a20100924d2005      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aNetwork Analysis $eMethodological Foundations /$fedited by Ulrik Brandes, Thomas Erlebach
205   $a1st ed. 2005.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2005.
215   $a1 online resource (XII, 472 p.) 
225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v3418
300   $aBibliographic Level Mode of Issuance: Monograph
311 08$aPrinted edition: 9783540249795 
320   $aIncludes bibliographical references (p. [439]-466) and index.
327   $aFundamentals -- I Elements -- Centrality Indices -- Algorithms for Centrality Indices -- Advanced Centrality Concepts -- II Groups -- Local Density -- Connectivity -- Clustering -- Role Assignments -- Blockmodels -- Network Statistics -- Network Comparison -- Network Models -- Spectral Analysis -- Robustness and Resilience.
330   $a?Network? is a heavily overloaded term, so that ?network analysis? means different things to different people. Specific forms of network analysis are used in the study of diverse structures such as the Internet, interlocking directorates, transportation systems, epidemic spreading, metabolic pathways, the Web graph, electrical circuits, project plans, and so on. There is, however, a broad methodological foundation which is quickly becoming a prerequisite for researchers and practitioners working with network models. From a computer science perspective, network analysis is applied graph theory. Unlike standard graph theory books, the content of this book is organized according to methods for specific levels of analysis (element, group, network) rather than abstract concepts like paths, matchings, or spanning subgraphs. Its topics therefore range from vertex centrality to graph clustering and the evolution of scale-free networks. In 15 coherent chapters, this monograph-like tutorial book introduces and surveys the concepts and methods that drive network analysis, and is thus the first book to do so from a methodological perspective independent of specific application areas.
410  0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v3418
606   $aComputer science?Mathematics
606   $aDiscrete mathematics
606   $aComputer networks
606   $aArtificial intelligence?Data processing
606   $aAlgorithms
606   $aDiscrete Mathematics in Computer Science
606   $aComputer Communication Networks
606   $aDiscrete Mathematics
606   $aData Science
606   $aAlgorithms
615  0$aComputer science?Mathematics.
615  0$aDiscrete mathematics.
615  0$aComputer networks.
615  0$aArtificial intelligence?Data processing.
615  0$aAlgorithms.
615 14$aDiscrete Mathematics in Computer Science.
615 24$aComputer Communication Networks.
615 24$aDiscrete Mathematics.
615 24$aData Science.
615 24$aAlgorithms.
676   $a004.2/1
702   $aBrandes$b Ulrik$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aErlebach$b Thomas$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910483456803321
996   $aNetwork Analysis$9772830
997   $aUNINA