Structural Characterization of Networks Using the Cat Cortex as an Example

Gorka Zamora-López*, Changsong ZHOU, Jürgen Kurths

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingChapterpeer-review

2 Citations (Scopus)


In this chapter, Graph Theory will be introduced using cat corticocortical connectivity data as an example. Distinct graph measures will be summarized and examples of their usage shown, as well as hints about the kind of information one can obtain from them. Special attention will be paid to conflicting points in graph theory that often generate confusion and some algorithmic tips will be provided. It is not our aim to introduce graph theory to the reader in a detailed manner, nor to reproduce what other authors have written in several extensive reviews (see Sect. 3.8). Some of the examples placed in this chapter referring to the cat cortex are unpublished material and thus, not to be regarded as established scientific results. Otherwise, references will be provided.

Original languageEnglish
Title of host publicationLectures in Supercomputational Neurosciences
Subtitle of host publicationDynamics in Complex Brain Networks
EditorsPeter Graben, Marco Thiel, Changsong Zhou, Jurgen Kurths
PublisherSpringer Berlin Heidelberg
Number of pages30
ISBN (Electronic)9783540731597
ISBN (Print)9783540731580, 9783642092169
Publication statusPublished - 2007

Publication series

NameUnderstanding Complex Systems
ISSN (Print)1860-0832
ISSN (Electronic)1860-0840

Scopus Subject Areas

  • Software
  • Computational Mechanics
  • Artificial Intelligence

User-Defined Keywords

  • Random Graph
  • Degree Distribution
  • Random Network
  • Real Network
  • Adjacency List


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