MNet has a close relationship with multiple kernel learning, which is a popular topic in machine learning [26], and it's also widely applied in biological data mining [5,14,25]. Wang et al. [27] introduced a method called Optimal Multiple Graphs learning (OMG) to integrate multiple graphs into a composite one for graph-based semi-supervised learning. Shiga et al. [28] proposed a method called LIG. LIG first partitions each individual graph into several locally informative subgraphs via soft spectral clustering and then integrates these subgraphs into a composite one for graph-based classification. A protein can have several different functions and these functions are inter-correlated, thus protein function prediction from multiple data sources can also be transformed into a multi-label multiple kernel learning problem [3,25]. Multi-label multiple kernel learning methods often learn a composite kernel for each binary label and thus have a complexity linear to the number of labels. Bucak et al. [29] suggested a method called multiple kernel learning by stochastic approximation, whose complexity is sub-linear to the number of labels.