Improving clustering in temporal networks through an evolutionary algorithm

The dynamic stochastic blockmodel is commonly used to analyze longitudinal network data when multiple snapshots are observed over time. The variational expectation-maximization (VEM) algorithm is typically employed for maximum likelihood inference to allocate nodes to groups dynamically. To address the problem of multiple local maxima, which may arise in this context, we propose modifying the VEM according to an evolutionary algorithm to explore the whole parameter space. A simulation study on dynamic networks and an application illustrate the proposal comparing the performance with that of the VEM algorithm.