  We propose a novel spectral convolutional neural network (CNN) model on graph
structured data, namely Distributed Feedback-Looped Networks (DFNets). This
model is incorporated with a robust class of spectral graph filters, called
feedback-looped filters, to provide better localization on vertices, while
still attaining fast convergence and linear memory requirements. Theoretically,
feedback-looped filters can guarantee convergence w.r.t. a specified error
bound, and be applied universally to any graph without knowing its structure.
Furthermore, the propagation rule of this model can diversify features from the
preceding layers to produce strong gradient flows. We have evaluated our model
using two benchmark tasks: semi-supervised document classification on citation
networks and semi-supervised entity classification on a knowledge graph. The
experimental results show that our model considerably outperforms the
state-of-the-art methods in both benchmark tasks over all datasets.
