In this study, we build a composite network optimized for a linear neighborhood propagation classifier. The resulting method is called MNet. MNet iteratively optimizes the weights assigned to the individual networks and the loss of the classifier according to a unified objective function. We show that the unified objective function can boost the accuracy of protein function prediction according to several evaluation criteria. Furthermore, MNet is more robust than other related approaches for a wide range of parameter values.