The Resource MaximumEntropy Networks : Pattern Detection, Network Reconstruction and Graph Combinatorics, by Tiziano Squartini, Diego Garlaschelli, (electronic resource)
MaximumEntropy Networks : Pattern Detection, Network Reconstruction and Graph Combinatorics, by Tiziano Squartini, Diego Garlaschelli, (electronic resource)
 Summary
 This book is an introduction to maximumentropy 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 maximumentropy 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 higherorder 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 2hard3 combinatorial operations, such as the enumeration and unbiased sampling of graphs with given constraints, within a 2softened3 maximumentropy framework. A final chapter offers various overarching remarks and takehome 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
 Language
 eng
 Extent
 XII, 116 p. 34 illus., 31 illus. in color.
 Contents

 Introduction
 Maximumentropy 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?
 {u2018}Soft{u2019} combinatorial enumeration
 Quantifying ensemble (non)equivalence
 Breaking of equivalence between ensembles
 Implications of (non)equivalence for combinatorics
 2What then shall we choose?3 Hardness or softness?
 Concluding remarks
 Isbn
 9783319694382
 Label
 MaximumEntropy Networks : Pattern Detection, Network Reconstruction and Graph Combinatorics
 Title
 MaximumEntropy Networks
 Title remainder
 Pattern Detection, Network Reconstruction and Graph Combinatorics
 Statement of responsibility
 by Tiziano Squartini, Diego Garlaschelli
 Language
 eng
 Summary
 This book is an introduction to maximumentropy 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 maximumentropy 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 higherorder 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 2hard3 combinatorial operations, such as the enumeration and unbiased sampling of graphs with given constraints, within a 2softened3 maximumentropy framework. A final chapter offers various overarching remarks and takehome 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
 Image bit depth
 0
 Literary form
 non fiction
 Series statement
 SpringerBriefs in Complexity,
 Label
 MaximumEntropy Networks : Pattern Detection, Network Reconstruction and Graph Combinatorics, by Tiziano Squartini, Diego Garlaschelli, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 Introduction  Maximumentropy 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?  {u2018}Soft{u2019} combinatorial enumeration  Quantifying ensemble (non)equivalence  Breaking of equivalence between ensembles  Implications of (non)equivalence for combinatorics  2What then shall we choose?3 Hardness or softness?  Concluding remarks
 http://library.link/vocab/cover_art
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 unknown
 http://library.link/vocab/discovery_link
 {'f': 'http://opac.lib.rpi.edu/record=b4379982'}
 Extent
 XII, 116 p. 34 illus., 31 illus. in color.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9783319694382
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
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 online resource.
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.lib.rpi.edu/portal/MaximumEntropyNetworksPatternDetection/kGOnWbPIwPY/" typeof="WorkExample http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.lib.rpi.edu/portal/MaximumEntropyNetworksPatternDetection/kGOnWbPIwPY/">MaximumEntropy Networks : Pattern Detection, Network Reconstruction and Graph Combinatorics, by Tiziano Squartini, Diego Garlaschelli, (electronic resource)</a></span>  <span property="offers" typeOf="Offer"><span property="offeredBy" typeof="Library ll:Library" resource="http://link.lib.rpi.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.lib.rpi.edu/">Rensselaer Libraries</a></span></span></span></span></div>