Title :
Network Classes and Graph Complexity Measures
Author :
Dehmer, Matthias ; Borgert, Stephan ; Emmert-Streib, Frank
Author_Institution :
Center for Math., Univ. of Coimbra, Coimbra, Portugal
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;
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
DOI :
10.1109/CANS.2008.17
