Current techniques on integrating multiple networks can be mainly divided into two categories: (i) several approaches model the composite kernel optimization and the final predictor training as separate problems. As such they may not necessarily result in optimal predictors [15,16]. (ii) Some methods optimize the composite network and the predictor for each functional label separately [5,14]. Since protein functions are inter-correlated and most functional labels often have a relatively small number of member proteins, these algorithms ignore the interrelationship among labels, which can often be used to boost the prediction accuracy [3,19]. Furthermore, they have to resort to time consuming special techniques (i.e., parameter tuning, regularization) to avoid the over-fitting problem and to optimize a composite network for each label.