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.
Original language | English |
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Title of host publication | Proc. - 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 |
Pages | 77-84 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 2008 |
Externally published | Yes |
Publication type | A4 Article in conference proceedings |
Event | 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 - Targu Mures, Mures, Finland Duration: 8 Nov 2008 → 10 Nov 2008 |
Conference
Conference | 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 |
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Country/Territory | Finland |
City | Targu Mures, Mures |
Period | 8/11/08 → 10/11/08 |
Keywords
- Entropy
- Network complexity measures
- Network modelling
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Biomedical Engineering