  Over the past two decades, there has been a tremendous increase in the growth
of representation learning methods for graphs, with numerous applications
across various fields, including bioinformatics, chemistry, and the social
sciences. However, current dynamic network approaches focus on discrete-time
networks or treat links in continuous-time networks as instantaneous events.
Therefore, these approaches have limitations in capturing the persistence or
absence of links that continuously emerge and disappear over time for
particular durations. To address this, we propose a novel stochastic process
relying on survival functions to model the durations of links and their
absences over time. This forms a generic new likelihood specification
explicitly accounting for intermittent edge-persistent networks, namely GraSSP:
Graph Representation with Sequential Survival Process. We apply the developed
framework to a recent continuous time dynamic latent distance model
characterizing network dynamics in terms of a sequence of piecewise linear
movements of nodes in latent space. We quantitatively assess the developed
framework in various downstream tasks, such as link prediction and network
completion, demonstrating that the developed modeling framework accounting for
link persistence and absence well tracks the intrinsic trajectories of nodes in
a latent space and captures the underlying characteristics of evolving network
structure.

Machine Learning;Artificial Intelligence;Social and Information Networks