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. © 2011 Springer-Verlag.
Cite
CITATION STYLE
Clémençon, S., De Arazoza, H., Rossi, F., & Tran, V. C. (2011). Visual mining of epidemic networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6692 LNCS, pp. 276–283). https://doi.org/10.1007/978-3-642-21498-1_35
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