LEADER 04488nam 22007575 450 001 996466698503316 005 20200705015703.0 010 $a3-540-87833-5 024 7 $a10.1007/978-3-540-87833-9 035 $a(CKB)1000000000547001 035 $a(SSID)ssj0000320247 035 $a(PQKBManifestationID)11274556 035 $a(PQKBTitleCode)TC0000320247 035 $a(PQKBWorkID)10347986 035 $a(PQKB)11415575 035 $a(DE-He213)978-3-540-87833-9 035 $a(MiAaPQ)EBC3063653 035 $a(PPN)13111963X 035 $a(EXLCZ)991000000000547001 100 $a20100301d2009 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aStructure in Complex Networks$b[electronic resource] /$fby Jörg Reichardt 205 $a1st ed. 2009. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2009. 215 $a1 online resource (XIII, 151 p.) 225 1 $aLecture Notes in Physics,$x0075-8450 ;$v766 300 $a"ISSN electronic edition 1616-6361." 311 $a3-540-87832-7 320 $aIncludes bibliographical references. 327 $ato Complex Networks -- Standard Approaches to Network Structure: Block Modeling -- A First Principles Approach to Block Structure Detection -- Diagonal Block Models as Cohesive Groups -- Modularity of Dense Random Graphs -- Modularity of Sparse Random Graphs -- Applications -- Conclusion and Outlook. 330 $aIn the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way. This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. In the subsequent chapters, a first-principles approach to graph clustering in complex networks is developed using methods from statistical physics and the reader will learn, that even today, this field significantly contributes to the understanding and resolution of the related statistical inference issues. Finally, an application chapter examines real-world networks from the economic realm to show how the network clustering process can be used to deal with large, sparse datasets where conventional analyses fail. 410 0$aLecture Notes in Physics,$x0075-8450 ;$v766 606 $aComputers 606 $aAlgorithms 606 $aArtificial intelligence 606 $aStatistical physics 606 $aDynamical systems 606 $aEconomic theory 606 $aTheory of Computation$3https://scigraph.springernature.com/ontologies/product-market-codes/I16005 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComplex Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/P33000 606 $aEconomic Theory/Quantitative Economics/Mathematical Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/W29000 606 $aStatistical Physics and Dynamical Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/P19090 615 0$aComputers. 615 0$aAlgorithms. 615 0$aArtificial intelligence. 615 0$aStatistical physics. 615 0$aDynamical systems. 615 0$aEconomic theory. 615 14$aTheory of Computation. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aArtificial Intelligence. 615 24$aComplex Systems. 615 24$aEconomic Theory/Quantitative Economics/Mathematical Methods. 615 24$aStatistical Physics and Dynamical Systems. 676 $a530.13 700 $aReichardt$b Jörg$4aut$4http://id.loc.gov/vocabulary/relators/aut$0564101 906 $aBOOK 912 $a996466698503316 996 $aStructure in complex networks$9949223 997 $aUNISA