We study suitable matrix functions to merge information that comes from different kinds of interactions encoded in multilayer graphs, and its effects in cluster identification. We consider a family of matrix functions, known as power means, and show that different means identify clusters under different settings of the stochastic block model. For instance, we show that a limit case identifies clusters if at least one layer is informative and remaining layers are potentially just noise.