00769nam0-22002891i-450-99000754100040332120070222133432.0000754100FED01000754100(Aleph)000754100FED0100075410020030814d1953----km-y0itay50------baengITy-------001yyAgricolture in the near eastdevelopment and outlookRomaFao1953IV, 78 p.28 cmMedio OrienteAgricolturaFAO17670ITUNINARICAUNIMARCBK990007541000403321G-04-046I.G. 5444ILFGEILFGEAgricolture in the near east684655UNINA04488nam 22007575 450 99646669850331620200705015703.03-540-87833-510.1007/978-3-540-87833-9(CKB)1000000000547001(SSID)ssj0000320247(PQKBManifestationID)11274556(PQKBTitleCode)TC0000320247(PQKBWorkID)10347986(PQKB)11415575(DE-He213)978-3-540-87833-9(MiAaPQ)EBC3063653(PPN)13111963X(EXLCZ)99100000000054700120100301d2009 u| 0engurnn|008mamaatxtccrStructure in Complex Networks[electronic resource] /by Jörg Reichardt1st ed. 2009.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2009.1 online resource (XIII, 151 p.) Lecture Notes in Physics,0075-8450 ;766"ISSN electronic edition 1616-6361."3-540-87832-7 Includes bibliographical references.to 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.In 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.Lecture Notes in Physics,0075-8450 ;766ComputersAlgorithmsArtificial intelligenceStatistical physicsDynamical systemsEconomic theoryTheory of Computationhttps://scigraph.springernature.com/ontologies/product-market-codes/I16005Algorithm Analysis and Problem Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/I16021Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Complex Systemshttps://scigraph.springernature.com/ontologies/product-market-codes/P33000Economic Theory/Quantitative Economics/Mathematical Methodshttps://scigraph.springernature.com/ontologies/product-market-codes/W29000Statistical Physics and Dynamical Systemshttps://scigraph.springernature.com/ontologies/product-market-codes/P19090Computers.Algorithms.Artificial intelligence.Statistical physics.Dynamical systems.Economic theory.Theory of Computation.Algorithm Analysis and Problem Complexity.Artificial Intelligence.Complex Systems.Economic Theory/Quantitative Economics/Mathematical Methods.Statistical Physics and Dynamical Systems.530.13Reichardt Jörgauthttp://id.loc.gov/vocabulary/relators/aut564101BOOK996466698503316Structure in complex networks949223UNISA