Hierarchical clustering for graph visualization

Stéphan Clémençon; Hector De Arazoza; Fabrice Rossi; Viet Chi Tran

Hierarchical clustering for graph visualization

Stéphan Clémençon
, Hector De Arazoza
, Fabrice Rossi
, Viet Chi Tran

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

This paper describes a graph visualization methodology based on hierarchical maximal modularity clustering, with interactive and significant coarsening and refining possibilities. An application of this method to HIV epidemic analysis in Cuba is outlined.

langue originale	Anglais
titre	Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010
Pages	227-232
Nombre de pages	6
état	Publié - 1 déc. 2010
Modification externe	Oui

Série de publications

Nom	ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

SDG des Nations Unies

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Clémençon, S., De Arazoza, H., Rossi, F., & Tran, V. C. (2010). Hierarchical clustering for graph visualization. Dans Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010 (p. 227-232). (ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Clémençon, Stéphan ; De Arazoza, Hector ; Rossi, Fabrice et al. / Hierarchical clustering for graph visualization. Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. 2010. p. 227-232 (ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

@inproceedings{2f95131c684743dbae3e77e17b2d88ab,

title = "Hierarchical clustering for graph visualization",

abstract = "This paper describes a graph visualization methodology based on hierarchical maximal modularity clustering, with interactive and significant coarsening and refining possibilities. An application of this method to HIV epidemic analysis in Cuba is outlined.",

author = "St{\'e}phan Cl{\'e}men{\c c}on and \{De Arazoza\}, Hector and Fabrice Rossi and Tran, \{Viet Chi\}",

year = "2010",

month = dec,

day = "1",

language = "English",

isbn = "9782874190445",

series = "ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

pages = "227--232",

booktitle = "Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010",

}

Clémençon, S, De Arazoza, H, Rossi, F & Tran, VC 2010, Hierarchical clustering for graph visualization. Dans Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, p. 227-232.

Hierarchical clustering for graph visualization. / Clémençon, Stéphan; De Arazoza, Hector; Rossi, Fabrice et al.
Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. 2010. p. 227-232 (ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Hierarchical clustering for graph visualization

AU - Clémençon, Stéphan

AU - De Arazoza, Hector

AU - Rossi, Fabrice

AU - Tran, Viet Chi

PY - 2010/12/1

Y1 - 2010/12/1

N2 - This paper describes a graph visualization methodology based on hierarchical maximal modularity clustering, with interactive and significant coarsening and refining possibilities. An application of this method to HIV epidemic analysis in Cuba is outlined.

AB - This paper describes a graph visualization methodology based on hierarchical maximal modularity clustering, with interactive and significant coarsening and refining possibilities. An application of this method to HIV epidemic analysis in Cuba is outlined.

UR - https://www.scopus.com/pages/publications/84887050059

M3 - Conference contribution

AN - SCOPUS:84887050059

SN - 9782874190445

T3 - ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

SP - 227

EP - 232

BT - Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010

ER -

Clémençon S, De Arazoza H, Rossi F, Tran VC. Hierarchical clustering for graph visualization. Dans Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. 2010. p. 227-232. (ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Hierarchical clustering for graph visualization

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SDG des Nations Unies

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