This shows that the method can uncover the latent community structure in the different datasets. Within each community, nodes still exhibit degree heterogeneity as shown in Figure \[fig:adjacency\] (e)-(h). where the nodes are sorted within each block according to their estimated sociability $\widehat w_{i0}$. The ability of the approach to uncover latent structure was illustrated by @Todeschini2016, who demonstrate that models which do not account for degree heterogeneity, cannot capture latent community estimation but they rather cluster the nodes based on their degree. We also look at the posterior predictive degree distribution based on the estimated hyperparameters, and compare it to the empirical degree distribution in the test set. The results are reported in Figure \[fig:adjacency\] (i)-(l) showing a reasonable fit to the degree distribution. Finally we report the $95\%$ posterior credible intervals (PCI) for the sparsity parameter $\sigma$ for all datasets. Each PCI is $(-0.69, -0.50), (-0.35, -0.20 ),(0.15, 0.18), (0.51,  0.57)$ respectively. The range of $\sigma$ is $ (-\infty,1)$. [Email]{} and [college]{} give negative values corresponding to denser networks whereas [Math]{} and [Ubuntu]{} datasets are sparser.

Conclusion
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We have presented a novel statistical model for temporal interaction data which captures multiple important features observed in such datasets, 