Edge Exchangeable Temporal Network Models

We propose a dynamic edge exchangeable network model that can capture sparse connections observed in real temporal networks, in contrast to existing models which are dense. The model achieved superior link prediction accuracy when compared to a dynamic variant of the blockmodel, and is able to extract interpretable time-varying community structures. In addition to sparsity, the model accounts for the effect of social influence on vertices’ future behaviours. Compared to the dynamic blockmodels, our model has a smaller latent space. The compact latent space requires a smaller number of parameters to be estimated in variational inference and results in a computationally friendly inference algorithm.