We find that the objective minimized by LP-ZGL (Equation 1) is underregularized, i.e., its model parameters (Ŷ ) are not constrained enough, compared to MAD (Equation 3, specifically the third term), resulting in overfitting in case of highly connected graphs.
mAD is able to avoid such overfitting because of its minimization of a well regularized objective (Equation 3).