A Dynamic Edge Exchangeable Model for Sparse Temporal Networks
This work addresses the challenge of sparse connections in temporal networks for researchers in network analysis, though it appears incremental by building on existing exchangeable models.
The authors tackled the problem of modeling sparse temporal networks by proposing a dynamic edge exchangeable model, which achieved superior link prediction accuracy compared to dynamic blockmodels and extracted interpretable time-varying community structures.
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.