Visual Mining of Epidemic Networks

TL;DR

This paper presents an interactive graph visualization technique leveraging maximal modularity clustering to explore large epidemic networks, revealing relationships between HIV spread and patients' sexual orientation.

Contribution

It introduces a novel visualization approach that combines clustering with statistical testing to analyze epidemic networks interactively.

Findings

Visualization exposes links between HIV propagation and sexual orientation.

Clustering helps identify community structures in epidemic networks.

Interactive exploration aids in understanding complex epidemiological data.

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.