Reviews: Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs

The paper discusses how to solve semi-supervised learning with multi-layer graphs. For single-layer graphs, this is achieved by label regression regularized by Laplacian matrix. For multi-layer, the paper argues that it should use a power mean Laplacian instead of the plain additive sum of Laplacians in each layer. This generalizes prior work including using the harmonic means. Some theoretical discussions follow under the assumptions from Multilayer Stochastic Block Model (MSBM), showing that specificity and robustness trade-offs can be achieved by adjusting the power parameter.