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.
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Clémençon, S., De Arazoza, H., Rossi, F., Tran, VC. (2011). Visual Mining of Epidemic Networks. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_35
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DOI: https://doi.org/10.1007/978-3-642-21498-1_35
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