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 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010
Pages	227-232
Number of pages	6
Publication status	Published - 1 Dec 2010
Externally published	Yes

Publication series

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

UN SDGs

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

SDG 3 Good Health and Well-being

Cite this

Clémençon, S., De Arazoza, H., Rossi, F., & Tran, V. C. (2010). Hierarchical clustering for graph visualization. In Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010 (pp. 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. pp. 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. in 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, pp. 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).

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 - 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. In 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

Abstract

Publication series

UN SDGs

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