Overlapping mixture models for network data (manet) with covariates adjustment

Network data often come in the form of actor-event information, where two types of nodes comprise the very fabric of the network. Examples of such networks are: people voting in an election, users liking/disliking media content, or, more generally, individuals - actors - attending events. Interest lies in discovering communities among these actors, based on their patterns of attendance to the considered events. To achieve this goal, we propose an extension of the model introduced in [5]: our contribution injects covariates into the model, leveraging on parsimony for the parameters and giving insights about the influence of such characteristics on the attendances. We assess the performance of our approach in a simulated environment.