Extracting Information from Weighted Contact Networks via Genetic Algorithms

Abstract

Epidemic contact tracing examines the movement of infection through a population based upon links in a contact network, and weighted networks represent the potential of transfer of the contagion. Graph compression reduces the size of a network by merging groups of nodes into supernodes. This study considers the use of genetic algorithms to select the nodes to be merged, grouping together highly connected sections of the graphs. Examined is a dataset that is extracted from contacts that occurred during several days of the "Infectious: Stay Away" event. The incorporation of weights, to indicate the strength of interactions between individuals, is an important contribution of this work. The demonstrated outcomes are that by including weighted information on the edges, there is more effective detection of highly interacting subgroups when compared to the unweighted version of graphs. These methods not only compress the networks with a low rate of distortion, but also the identification of supernodes in the networks allows for better targeting of interventions by public health upon individuals in such groups. This is crucial because when one member becomes infected, all members of the group are exposed to the contagion.