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000827658 019__ $$a1012939305$$a1013173687$$a1013480899$$a1013825594$$a1017836917$$a1032267892
000827658 020__ $$a9783319694382$$q(electronic book)
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000827658 0247_ $$a10.1007/978-3-319-69438-2$$2doi
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000827658 1001_ $$aSquartini, Tiziano,$$eauthor.
000827658 24510 $$aMaximum-entropy networks :$$bpattern detection, network reconstruction and graph combinatorics /$$cTiziano Squartini, Diego Garlaschelli.
000827658 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2017]
000827658 264_4 $$c©2017
000827658 300__ $$a1 online resource (xii, 116 pages) :$$billustrations.
000827658 336__ $$atext$$btxt$$2rdacontent
000827658 337__ $$acomputer$$bc$$2rdamedia
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000827658 4901_ $$aSpringerBriefs in complexity,$$x2191-5326
000827658 504__ $$aIncludes bibliographical references and index.
000827658 5050_ $$aIntroduction -- Maximum-entropy ensembles of graphs -- Constructing constrained graph ensembles: why and how? -- Comparing models obtained from different constraints -- Pattern detection -- Detecting assortativity and clustering -- Detecting dyadic motifs -- Detecting triadic motifs -- Some extensions to weighted networks -- Network reconstruction -- Reconstructing network properties from partial information -- The Enhanced Configuration Model -- Further reducing the observational requirements -- Graph combinatorics -- A dual route to combinatorics? -- 'Soft' combinatorial enumeration -- Quantifying ensemble (non)equivalence -- Breaking of equivalence between ensembles -- Implications of (non)equivalence for combinatorics -- "What then shall we choose?" Hardness or softness? -- Concluding remarks.
000827658 506__ $$aAccess limited to authorized users.
000827658 520__ $$aThis book is an introduction to maximum-entropy models of random graphs with given topological properties and their applications. Its original contribution is the reformulation of many seemingly different problems in the study of both real networks and graph theory within the unified framework of maximum entropy. Particular emphasis is put on the detection of structural patterns in real networks, on the reconstruction of the properties of networks from partial information, and on the enumeration and sampling of graphs with given properties. After a first introductory chapter explaining the motivation, focus, aim and message of the book, chapter 2 introduces the formal construction of maximum-entropy ensembles of graphs with local topological constraints. Chapter 3 focuses on the problem of pattern detection in real networks and provides a powerful way to disentangle nontrivial higher-order structural features from those that can be traced back to simpler local constraints. Chapter 4 focuses on the problem of network reconstruction and introduces various advanced techniques to reliably infer the topology of a network from partial local information. Chapter 5 is devoted to the reformulation of certain "hard" combinatorial operations, such as the enumeration and unbiased sampling of graphs with given constraints, within a "softened" maximum-entropy framework. A final chapter offers various overarching remarks and take-home messages. By requiring no prior knowledge of network theory, the book targets a broad audience ranging from PhD students approaching these topics for the first time to senior researchers interested in the application of advanced network techniques to their field.
000827658 588__ $$aResource, viewed February 1, 2018.
000827658 650_0 $$aRandom graphs.
000827658 650_0 $$aSystem analysis.
000827658 650_0 $$aGraph theory.
000827658 650_0 $$aComputational complexity.
000827658 650_0 $$aSystem theory.
000827658 7001_ $$aGarlaschelli, Diego,$$eauthor.
000827658 77608 $$iPrint version: $$z9783319694368
000827658 830_0 $$aSpringerBriefs in complexity.
000827658 852__ $$bebk
000827658 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-69438-2$$zOnline Access$$91397441.1
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