Continuous Latent Position Models for Instantaneous Interactions
This provides a method for modeling dynamic interaction data, which is incremental as it extends existing latent position models to continuous time.
The authors tackled the problem of analyzing the timing and frequency of instantaneous interactions, such as in email or phone call networks, by developing a framework based on continuous latent position models, which estimates individual trajectories from observed data.
We create a framework to analyse the timing and frequency of instantaneous interactions between pairs of entities. This type of interaction data is especially common nowadays, and easily available. Examples of instantaneous interactions include email networks, phone call networks and some common types of technological and transportation networks. Our framework relies on a novel extension of the latent position network model: we assume that the entities are embedded in a latent Euclidean space, and that they move along individual trajectories which are continuous over time. These trajectories are used to characterize the timing and frequency of the pairwise interactions. We discuss an inferential framework where we estimate the individual trajectories from the observed interaction data, and propose applications on artificial and real data.