rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Maximum-Entropy Networks
    sub title: Pattern Detection, Network Reconstruction and Graph Combinatorics
  name:
    name part: Squartini, Tiziano.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Garlaschelli, Diego.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XII, 116 p. 34 illus., 31 illus. in color. online resource.
  abstract: 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.
  table of contents: 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.
  note: by Tiziano Squartini, Diego Garlaschelli.
  subject:
    topic: Physics
  subject:
    topic: System theory
  subject:
    topic: Graph theory
  subject:
    topic: Complexity, Computational
  subject:
    topic: Physics
  subject:
    topic: Applications of Graph Theory and Complex Networks
  subject:
    topic: Statistical Physics and Dynamical Systems
  subject:
    topic: Complex Systems
  subject:
    topic: Graph Theory
  subject:
    topic: Complexity
  classification: QC1-QC999
  classification: 621
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Complexity
  identifier: 9783319694382
  identifier: http://dx.doi.org/10.1007/978-3-319-69438-2
  record info:
    record creation date: 171121
    record change date: 20171121234719.0
    record identifier: 978-3-319-69438-2