  We propose a novel stacked generalization (stacking) method as a dynamic
ensemble technique using a pool of heterogeneous classifiers for node label
classification on networks. The proposed method assigns component models a set
of functional coefficients, which can vary smoothly with certain topological
features of a node. Compared to the traditional stacking model, the proposed
method can dynamically adjust the weights of individual models as we move
across the graph and provide a more versatile and significantly more accurate
stacking model for label prediction on a network. We demonstrate the benefits
of the proposed model using both a simulation study and real data analysis.
