TY - GEN
T1 - Network classes and graph complexity measures
AU - Dehmer, Matthias
AU - Borgert, Stephan
AU - Emmert-Streib, Frank
PY - 2008/11/10
Y1 - 2008/11/10
N2 - 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.
AB - 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.
KW - Entropy
KW - Network complexity measures
KW - Network modelling
UR - https://www.scopus.com/pages/publications/70350557662
U2 - 10.1109/CANS.2008.17
DO - 10.1109/CANS.2008.17
M3 - Conference contribution
AN - SCOPUS:70350557662
SN - 9780769536217
T3 - 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
SP - 77
EP - 84
BT - 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
T2 - 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
Y2 - 8 November 2008 through 10 November 2008
ER -