國立虎尾科技大學 |

Maximum-entropy networks = pattern detection, network reconstruction and graph combinatorics /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Maximum-entropy networks/ by Tiziano Squartini, Diego Garlaschelli.
Reminder of title:	pattern detection, network reconstruction and graph combinatorics /
Author:	Squartini, Tiziano.
other author:	Garlaschelli, Diego.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xii, 116 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Maximum entropy method. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-69438-2
ISBN:	9783319694382

Maximum-entropy networks = pattern detection, network reconstruction and graph combinatorics /
Squartini, Tiziano.

Maximum-entropy networkspattern detection, network reconstruction and graph combinatorics /[electronic resource] :by Tiziano Squartini, Diego Garlaschelli. - Cham :Springer International Publishing :2017. - xii, 116 p. :ill., digital ;24 cm. - Springerbriefs in complexity,2191-5326. - Springerbriefs in complexity..

Introduction -- 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.

This 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.

ISBN: 9783319694382

Standard No.: 10.1007/978-3-319-69438-2doiSubjects--Topical Terms:

580204
Maximum entropy method.

LC Class. No.: Q370

Dewey Class. No.: 003.54

Maximum-entropy networks = pattern detection, network reconstruction and graph combinatorics /
LDR:03587nam a2200337 a 4500 001 922208
003 DE-He213
005 20180523153021.0
006 m d
007 cr nn 008maaau
008 190624s2017 gw s 0 eng d
020 $a 9783319694382 $q (electronic bk.)
020 $a 9783319694368 $q (paper)
024 7 $a 10.1007/978-3-319-69438-2 $2 doi
035 $a 978-3-319-69438-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q370
072 7 $a PHU $2 bicssc
072 7 $a PBKD $2 bicssc
072 7 $a SCI064000 $2 bisacsh
082 0 4 $a 003.54 $2 23
090 $a Q370 $b .S773 2017
100 1 $a Squartini, Tiziano. $3 1197646
245 1 0 $a Maximum-entropy networks $h [electronic resource] : $b pattern detection, network reconstruction and graph combinatorics / $c by Tiziano Squartini, Diego Garlaschelli.
260 $a Cham : $c 2017. $b Springer International Publishing : $b Imprint: Springer,
300 $a xii, 116 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springerbriefs in complexity, $x 2191-5326
505 0 $a Introduction -- 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.
520 $a This 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.
650 0 $a Maximum entropy method. $3 580204
650 1 4 $a Physics. $3 564049
650 2 4 $a Applications of Graph Theory and Complex Networks. $3 1113468
650 2 4 $a Statistical Physics and Dynamical Systems. $3 1114011
650 2 4 $a Complex Systems. $3 888664
650 2 4 $a Graph Theory. $3 786670
650 2 4 $a Complexity. $3 669595
700 1 $a Garlaschelli, Diego. $3 1197647
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Springerbriefs in complexity. $3 1197648
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-69438-2
950 $a Physics and Astronomy (Springer-11651)