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: Structure in Complex Networks
  name:
    name part: Reichardt, J.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2009
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIII, 151 p. online resource.
  abstract: In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way. This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. In the subsequent chapters, a first-principles approach to graph clustering in complex networks is developed using methods from statistical physics and the reader will learn, that even today, this field significantly contributes to the understanding and resolution of the related statistical inference issues. Finally, an application chapter examines real-world networks from the economic realm to show how the network clustering process can be used to deal with large, sparse datasets where conventional analyses fail.
  table of contents: to Complex Networks -- Standard Approaches to Network Structure: Block Modeling -- A First Principles Approach to Block Structure Detection -- Diagonal Block Models as Cohesive Groups -- Modularity of Dense Random Graphs -- Modularity of Sparse Random Graphs -- Applications -- Conclusion and Outlook.
  note: by J. Reichardt.
  subject:
    topic: Computer science
  subject:
    topic: Computers
  subject:
    topic: Algorithms
  subject:
    topic: Artificial intelligence
  subject:
    topic: Statistical physics
  subject:
    topic: Dynamical systems
  subject:
    topic: Economic theory
  subject:
    topic: Computer Science
  subject:
    topic: Theory of Computation
  subject:
    topic: Algorithm Analysis and Problem Complexity
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Statistical Physics, Dynamical Systems and Complexity
  subject:
    topic: Economic Theory/Quantitative Economics/Mathematical Methods
  classification: QA75.5-76.95
  classification: 004.0151
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Physics, 766
  identifier: 9783540878339
  identifier: http://dx.doi.org/10.1007/978-3-540-87833-9
  record info:
    record creation date: 100301
    record change date: 20151204142452.0
    record identifier: 978-3-540-87833-9