Implementation of the ComE algorithm using a Bayesian Gaussian mixture model and variational inference (BGMM+VI) for community embedding instead of a non-Bayesian Gaussian mixture model and expectation maximization (GMM+EM). The advantage of BGMM+VI over GMM+EM is that the hyper-parameter number of communities K acts as an upper bound and unused communities are discarded using the trade-off hyper-parameter weight concentration prior.
