Dynamic Stacked Generalization for Node Classification on Networks
This work addresses the problem of improving classification accuracy in network data for researchers and practitioners, though it appears incremental as it builds on existing stacking techniques.
The authors tackled node label classification on networks by proposing a dynamic stacked generalization method that adjusts model weights based on topological features, achieving significantly more accurate predictions compared to traditional stacking.
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.