Network classes and graph complexity measures

Matthias Dehmer; Stephan Borgert; Frank Emmert-Streib

doi:10.1109/CANS.2008.17

Network classes and graph complexity measures

Matthias Dehmer, Stephan Borgert, Frank Emmert-Streib

Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

2 Sitaatiot (Scopus)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	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
Sivut	77-84
Sivumäärä	8
DOI - pysyväislinkit	https://doi.org/10.1109/CANS.2008.17
Tila	Julkaistu - 2008
Julkaistu ulkoisesti	Kyllä
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	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, Suomi Kesto: 8 marrask. 2008 → 10 marrask. 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
Maa/Alue	Suomi
Kaupunki	Targu Mures, Mures
Ajanjakso	8/11/08 → 10/11/08

!!ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Biomedical Engineering

Pääsy asiakirjaan

10.1109/CANS.2008.17

Muut tiedostot ja linkit

Link to publication in Scopus

Siteeraa tätä

Dehmer, M., Borgert, S., & Emmert-Streib, F. (2008). Network classes and graph complexity measures. teoksessa 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 (Sivut 77-84). Artikkeli 5231507 https://doi.org/10.1109/CANS.2008.17

@inproceedings{dc3b7763f56b4062847303e23e330d1c,

title = "Network classes and graph complexity measures",

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 = "Entropy, Network complexity measures, Network modelling",

author = "Matthias Dehmer and Stephan Borgert and Frank Emmert-Streib",

year = "2008",

doi = "10.1109/CANS.2008.17",

language = "English",

isbn = "9780769536217",

pages = "77--84",

booktitle = "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",

note = "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 ; Conference date: 08-11-2008 Through 10-11-2008",

}

Dehmer, M, Borgert, S & Emmert-Streib, F 2008, Network classes and graph complexity measures. julkaisussa 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., 5231507, Sivut 77-84, 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, Suomi, 8/11/08. https://doi.org/10.1109/CANS.2008.17

Network classes and graph complexity measures. / Dehmer, Matthias; Borgert, Stephan; Emmert-Streib, Frank.
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. 2008. s. 77-84 5231507.

Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

TY - GEN

T1 - Network classes and graph complexity measures

AU - Dehmer, Matthias

AU - Borgert, Stephan

AU - Emmert-Streib, Frank

PY - 2008

Y1 - 2008

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 - http://www.scopus.com/inward/record.url?scp=70350557662&partnerID=8YFLogxK

U2 - 10.1109/CANS.2008.17

DO - 10.1109/CANS.2008.17

M3 - Conference contribution

AN - SCOPUS:70350557662

SN - 9780769536217

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 -

Network classes and graph complexity measures

Abstrakti

Conference

!!ASJC Scopus subject areas

Pääsy asiakirjaan

Muut tiedostot ja linkit

Sormenjälki

Siteeraa tätä