Tampere University of Technology

TUTCRIS Research Portal

Network classes and graph complexity measures

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationProc. - 2008 1st International Conference on Complexity and Intelligence of the Artificial and Natural Complex Systems. Medical Applications of the Complex Systems. Biomedical Computing, CANS 2008
Pages77-84
Number of pages8
DOIs
Publication statusPublished - 2008
Externally publishedYes
Publication typeA4 Article in a conference publication
Event2008 1st International Conference on Complexity and Intelligence of the Artificial and Natural Complex Systems. Medical Applications of the Complex Systems. Biomedical Computing, CANS 2008 - Targu Mures, Mures, Finland
Duration: 8 Nov 200810 Nov 2008

Conference

Conference2008 1st International Conference on Complexity and Intelligence of the Artificial and Natural Complex Systems. Medical Applications of the Complex Systems. Biomedical Computing, CANS 2008
CountryFinland
CityTargu Mures, Mures
Period8/11/0810/11/08

Abstract

In this paper, we propose an information-theoretic approach to discriminate graph classes structurally. For this, we use a measure for determining the structural information content of graphs. This complexity measure is based on a special information functional that quantifies certain structural information of a graph. To demonstrate that the complexity measure captures structural information meaningfully, we interpret some numerical results.

Keywords

  • Entropy, Network complexity measures, Network modelling