Abstract
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 (seeSect. 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Some of the examples placed in this chapter referring to the cat cortex are unpublished material and thus, not to be regarded as established scientific results.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 language | English |
|---|---|
| Title of host publication | Lectures in Supercomputational Neurosciences |
| Subtitle of host publication | Dynamics in Complex Brain Networks |
| Editors | Peter Graben, Changsong Zhou, Marco Thiel, Jurgen Kurths |
| Publisher | Springer Berlin Heidelberg |
| Pages | 77-106 |
| Number of pages | 30 |
| Edition | 1st |
| ISBN (Electronic) | 9783540731597 |
| ISBN (Print) | 9783540731580, 9783642092169 |
| DOIs | |
| Publication status | Published - 19 Oct 2007 |
Publication series
| Name | Understanding Complex Systems |
|---|---|
| Volume | 2008 |
| ISSN (Print) | 1860-0832 |
| ISSN (Electronic) | 1860-0840 |
User-Defined Keywords
- Random Graph
- Degree Distribution
- Random Network
- Real Network
- Adjacency List
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