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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 19th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2011
Publisher	ESANN (i6doc.com)
Pages	227-232
Number of pages	6
ISBN (Electronic)	9782874190445
Publication status	Published - 1 Jan 2011
Externally published	Yes
Event	19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2011 - Bruges, Belgium Duration: 27 Apr 2011 → 29 Apr 2011

Publication series

Name	ESANN 2011 - 19th European Symposium on Artificial Neural Networks

Conference

Conference	19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2011
Country/Territory	Belgium
City	Bruges
Period	27/04/11 → 29/04/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@inproceedings{13cd18352a65443391ec57fd7255ade9,

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\}",

note = "Publisher Copyright: {\textcopyright} European Symposium on Artificial Neural Networks. All rights reserved.; 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2011 ; Conference date: 27-04-2011 Through 29-04-2011",

year = "2011",

month = jan,

day = "1",

language = "English",

series = "ESANN 2011 - 19th European Symposium on Artificial Neural Networks",

publisher = "ESANN (i6doc.com)",

pages = "227--232",

booktitle = "Proceedings of the 19th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2011",

}

Clémençon, S, de Arazoza, H, Rossi, F & Tran, VC 2011, Hierarchical clustering for graph visualization. in Proceedings of the 19th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2011. ESANN 2011 - 19th European Symposium on Artificial Neural Networks, ESANN (i6doc.com), pp. 227-232, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2011, Bruges, Belgium, 27/04/11.

Hierarchical clustering for graph visualization. / Clémençon, Stéphan; de Arazoza, Hector; Rossi, Fabrice et al.
Proceedings of the 19th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2011. ESANN (i6doc.com), 2011. p. 227-232 (ESANN 2011 - 19th European Symposium on Artificial Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Hierarchical clustering for graph visualization

AU - Clémençon, Stéphan

AU - de Arazoza, Hector

AU - Rossi, Fabrice

AU - Tran, Viet Chi

PY - 2011/1/1

Y1 - 2011/1/1

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.

M3 - Conference contribution

AN - SCOPUS:85088920240

T3 - ESANN 2011 - 19th European Symposium on Artificial Neural Networks

SP - 227

EP - 232

BT - Proceedings of the 19th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2011

PB - ESANN (i6doc.com)

T2 - 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2011

Y2 - 27 April 2011 through 29 April 2011

ER -

Hierarchical clustering for graph visualization

Abstract

Publication series

Conference

UN SDGs

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Cite this