&\
\

We perform posterior inference using a Markov chain Monte Carlo algorithm. For our Hawkes-CCRM model, we follow the two-step procedure described in Section \[sec:inference\]. For each dataset, there is some background information in order to guide the choice of the number $p$ of communities. The number of communities $p$ is set to $p=4$ for the [Email]{} dataset, as there are 4 departments at the institution, $p=2$ for the [College]{} dataset corresponding to the two genders, and $p=3$ for the [Math]{} and [Ubuntu]{} datasets, corresponding to the three different types of possible interactions. We follow @Todeschini2016 regarding the choice of the MCMC tuning parameters and initialise the MCMC algorithm with the estimates obtained by running a MCMC algorithm with $p = 1$ feature with fewer iterations. For all experiments we run 2 chains in parallel for each stage of the inference. We use 100000 iterations for the first stage and 10000 for the second one. For the Hawkes-IRM model, we also use a similar two-step procedure, which first obtains a point estimate of the parameters and hyperparameters of the IRM, then estimates the parameters of the Hawkes process given this point estimate. This allows us to scale this approach 