MaximumEntropy Networks : Pattern Detection, Network Reconstruction and Graph Combinatorics
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The work MaximumEntropy Networks : Pattern Detection, Network Reconstruction and Graph Combinatorics represents a distinct intellectual or artistic creation found in Boston University Libraries. This resource is a combination of several types including: Work, Language Material, Books.
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MaximumEntropy Networks : Pattern Detection, Network Reconstruction and Graph Combinatorics
Resource Information
The work MaximumEntropy Networks : Pattern Detection, Network Reconstruction and Graph Combinatorics represents a distinct intellectual or artistic creation found in Boston University Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 MaximumEntropy Networks : Pattern Detection, Network Reconstruction and Graph Combinatorics
 Title remainder
 Pattern Detection, Network Reconstruction and Graph Combinatorics
 Statement of responsibility
 by Tiziano Squartini, Diego Garlaschelli
 Subject

 Complex Systems
 Computational complexity
 Physics
 Statistical Physics and Dynamical Systems
 Graph Theory
 Complexity
 Graph Theory
 Graph theory
 Computational complexity
 Graph theory
 System theory
 Electronic resources
 Graph theory
 Physics
 System theory
 Graph Theory
 Physics
 Applications of Graph Theory and Complex Networks
 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 “hard” combinatorial operations, such as the enumeration and unbiased sampling of graphs with given constraints, within a “softened” 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
 LC call number
 QC1QC999
 Literary form
 non fiction
 Series statement
 SpringerBriefs in Complexity,
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