Network Classes and Graph Complexity Measures

Author

Dehmer, Matthias ; Borgert, Stephan ; Emmert-Streib, Frank

Author_Institution

Center for Math., Univ. of Coimbra, Coimbra, Portugal

fYear

2008

fDate

8-10 Nov. 2008

Firstpage

77

Lastpage

84

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

computational complexity; graph theory; network theory (graphs); discriminate graph class; graph complexity measure; information-theoretic approach; network class; Artificial intelligence; Biomedical computing; Biomedical equipment; Biomedical measurements; Chemicals; Computational biology; Entropy; Mathematics; Medical services; Probability distribution; Entropy; Network Complexity Measures; Network Modelling;

fLanguage

English

Publisher

ieee

Conference_Titel

Complexity and Intelligence of the Artificial and Natural Complex Systems, Medical Applications of the Complex Systems, Biomedical Computing, 2008. CANS '08. First International Conference on

Conference_Location

Targu Mures, Mures

Print_ISBN

978-0-7695-3621-7

Type

conf

DOI

10.1109/CANS.2008.17

Filename

5231507