New Consistent and Asymptotically Normal Parameter Estimates for Random-Graph Mixture Models
随机图混合模型的一致且渐近正态的新参数估计
Summary Random-graph mixture models are very popular for modelling real data networks. Parameter estimation procedures usually rely on variational approximations, either combined with the expectation–maximization (EM) algorithm or with Bayesian approaches. Despite good results on synthetic data, the validity of the variational approximation is, however, not established. Moreover, these variational approaches aim at approximating the maximum likelihood or the maximum a…