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We propose a novel stacked generalization (stacking) method as a dynamicensemble technique using a pool of heterogeneous classifiers for node labelclassification on networks. The proposed method assigns component models a setof functional coefficients, which can vary smoothly with certain topologicalfeatures of a node. Compared to the traditional stacking model, the proposedmethod can dynamically adjust the weights of individual models as we moveacross the graph and provide a more versatile and significantly more accuratestacking model for label prediction on a network. We demonstrate the benefitsof the proposed model using both a simulation study and real data analysis.

Dynamic Stacked Generalization for Node Classification on Networks