Fast Maximum Likelihood Estimation via Equilibrium Expectation for Large Network Data.

ABSTRACT: A major line of contemporary research on complex networks is based on the development of statistical models that specify the local motifs associated with macro-structural properties observed in actual networks. This statistical approach becomes increasingly problematic as network size increases. In the context of current research on efficient estimation of models for large network data sets, we propose a fast algorithm for maximum likelihood estimation (MLE) that affords a significant increase in the size of networks amenable to direct empirical analysis. The algorithm we propose in this paper relies on properties of Markov chains at equilibrium, and for this reason it is called equilibrium expectation (EE). We demonstrate the performance of the EE algorithm in the context of exponential r

SUBMITTER: Byshkin M 

PROVIDER: S-EPMC6068132 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature