Title: A Dynamic Edge Exchangeable Model for Sparse Temporal Networks,
Abstract: 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 on
multiple data sets when compared to a dynamic variant of the blockmodel, and is
able to extract interpretable time-varying community structures from the data.
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.