Dynamic communicability predicts infectiousness

Using real, time-dependent social interaction data, we look at correlationsbetween some recently proposed dynamic centrality measures and summariesfrom large-scale epidemic simulations. The evolving network arises from email exchanges.The centrality measures, which are relatively inexpensive to compute, assignrankings to individual nodes based on their ability to broadcast information overthe dynamic topology. We compare these with node rankings based on infectiousnessthat arise when a full stochastic SI simulation is performed over the dynamicnetwork. More precisely, we look at the proportion of the network that a node is ableto infect over a fixed time period, and the length of time that it takes for a node to infecthalf the network.We find that the dynamic centrality measures are an excellent,and inexpensive, proxy for the full simulation-based measures.