Maximum Likelihood Estimation on Stochastic Blockmodels for Directed Graph Clustering

arXiv:2403.19516v1 Announce Type: cross
Abstract: This paper studies the directed graph clustering problem through the lens of statistics, where we formulate clustering as estimating underlying communities in the directed stochastic block model (DSBM). We conduct the maximum likelihood estimation (MLE) on the DSBM and thereby ascertain the most probable community assignment given the observed graph structure. In addition to the statistical point of view, we further establish the equivalence between this MLE formulation and a novel flow optimization heuristic, which …

abstract arxiv block clustering communities community cs.lg cs.si graph likelihood math.st maximum likelihood estimation mle paper statistics stat.ml stat.th stochastic studies through type