The data science of networks is a rapidly developing field with myriad applications. In neuroscience, the brain is commonly modeled as a connectome, a network of nodes connected by edges. While there have been thousands of papers on connectomics, the statistics of networks remains limited and...

We introduce a maximum L<italic>q</italic>-likelihood estimation (ML<italic>q</italic>E) of mixture models using our proposed expectation-maximization (EM) algorithm, namely the EM algorithm with L<italic>q</italic>-likelihood (EM-L<italic>q</italic>). Properties of the ML<italic>q</italic>E obtained from the proposed EM-L<italic>q</italic> are studied through simulated mixture model data....

We present a method to estimate block membership of nodes in a random graph generated by a stochastic blockmodel. We use an embedding procedure motivated by the random dot product graph model, a particular example of the latent position model. The embedding associates each node with a vector;...