Estimation of exchangeable graph models by stochastic blockmodel approximation

Stanley H. Chan, Thiago B. Costa, Edoardo M. Airoldi

Abstract: We consider a non-parametric perspective of analyzing network data. Our goal is to seek a limiting object of a sequence of exchangeable random arrays called the graphon. We propose a numerically efficient algorithm for estimating graphons and we show that the proposed algorithm yields a consistent estimate as the size of the graph grows. Preliminary experiments show that the algorithm is effective in estimating stochastic block-models and continuous graphons.