Network Classes and Graph Complexity Measures

被引：1
|
作者：
Dehmer, Matthias [1 ]
Borgert, Stephan [2 ]
Emmert-Streib, Frank [3 ]
机构：
[1] Univ Coimbra, Ctr Math, Apartado 3008, P-3001454 Coimbra, Portugal
[2] Tech Univ Darmstadt, Telecooperat Grp, D-64289 Darmstadt, Germany
[3] Queens Univ Belfast, Ctr Cancer Res & Cell Biol, Sch Med, Dentistry &Biomed Sci, Belfast, Antrim, North Ireland
来源：
2008 COMPLEXITY & INTELLIGENCE OF THE ARTIFICIAL & NATURAL COMPLEX SYSTEMS, MEDICAL APPLICATIONS OF THE COMPLEX SYSTEMS, BIOMEDICAL COMPUTING | 2008年
关键词：
Network Modelling; Network Complexity Measures; Entropy;
D O I：
10.1109/CANS.2008.17
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we propose an information-theoretic approach to discriminate graph classes structurally. For this, we use a measure for determining the structural info, Illation 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.
引用
收藏
页码：77 / +
页数：3
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