@inproceedings{ff8c4e4dbf1748388a057ea1077d1e92,
title = "Visual mining of epidemic networks",
abstract = "We show how an interactive graph visualization method based on maximal modularity clustering can be used to explore a large epidemic network. The visual representation is used to display statistical tests results that expose the relations between the propagation of HIV in a sexual contact network and the sexual orientation of the patients.",
author = "St{\'e}phan Cl{\'e}men{\c c}on and \{De Arazoza\}, Hector and Fabrice Rossi and Tran, \{Viet Chi\}",
year = "2011",
month = jun,
day = "8",
doi = "10.1007/978-3-642-21498-1\_35",
language = "English",
isbn = "9783642214974",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "276--283",
booktitle = "Advances in Computational Intelligence - 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Proceedings",
edition = "PART 2",
note = "11th International Work-Conference on on Artificial Neural Networks, IWANN 2011 ; Conference date: 08-06-2011 Through 10-06-2011",
}