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Abstract:
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We create a framework to analyse the timing and frequency of instantaneous interactions between pairs of entities. Nowadays, this type of time-stamped interaction data is especially common in many applied fields. Examples include email networks, phone call networks, proximity networks. The framework that we introduced is inspired by latent position network models: the entities are embedded in a continuous latent Euclidean space, and they move along individual trajectories that are also continuous over time. These trajectories are used to model the timing and frequency of the pairwise interactions. We discuss an inferential framework where we estimate the trajectories from the observed interaction data, and propose applications on artificial and real data.
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