Title: Alternating minimization algorithm for clustering mixture multilayer network
Authors: Teng Zhang - University of Central Florida (United States) [presenting]
Marianna Pensky - University of Central Florida (United States)
Abstract: A Mixture Multilayer Stochastic Block Model (MMLSBM) is considered, where layers can be partitioned into groups of similar networks, and networks in each group are equipped with a distinct Stochastic Block Model. The goal is to partition the multilayer network into clusters of similar layers, and to identify communities in those layers. The MMLSBM and a clustering methodology, TWIST, based on regularized tensor decomposition have been recently introduced. A different technique is presented, an alternating minimization algorithm (ALMA), that aims at simultaneous recovery of the layer partition, together with the estimation of the matrices of connection probabilities of the distinct layers. Compared to TWIST, ALMA achieves higher accuracy both theoretically and numerically.