The functional association network is an inherent and widely applied representation for encoding information of shared protein functions from high-throughput proteomic (or genomic) data sources (i.e., protein-protein interactions (PPI), protein sequences). In this representation, a node in the network corresponds to a protein, and the weights of the edges of connected nodes are specified to capture the evidence (or reliability) of shared functions derived from one data source. These weights are computed by a specific similarity metric for a given data source. For example, string kernels [17] for protein sequences, Pearson's correlation coefficients for gene expression profiles. In this way, each data source can be transformed into a network (or kernel). To leverage the networks derived from heterogeneous data sources to predict protein functions, some approaches first train individual classifiers on these networks and then use ensemble learning techniques to combine these classifiers [7,9,11,18]. Another set of algorithms first integrate these networks into a composite network and then train network-based learning methods [5,14-16]. In this study, we focus on the second kind of algorithms.