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This paper describes a novel approach to modeling homphily, i.e. the tendencyof nodes that share (or differ in) certain attributes to be linked; we considerdynamic networks in which nodes can be added over time but not removed. Ourapplication is to HIV genetic linkage analysis that has been used toinvestigate HIV transmission dynamics. In this setting, two HIV sequences fromdifferent persons with HIV (PWH) are said to be linked if the genetic distancebetween these sequences is less than a given threshold. Such linkage suggeststhat that the nodes representing the two infected PWH, are close to each otherin a transmission network; such proximity would imply that either one of theinfected people directly transmitted the virus to the other or indirectlytransmitted it through a small number of intermediaries. These viral geneticlinkage networks are dynamic in the sense that, over time, a group or clusterof genetically linked viral sequences may increase in size as new people areinfected by those in the cluster either directly or through intermediaries. Ourapproach makes use of a logistic model to describe homophily with regard todemographic and behavioral characteristics that is we investigate whethersimilarities (or differences) between PWH in these characteristics impacts theprobability that their sequences are be linked. Such analyses provideinformation about HIV transmission dynamics within a population.

Modeling Homophily in Dynamic Networks with Application to HIV Molecular Surveillance