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.

提出了一种基于谱卷积神经网络 (CNN) 的新型图结构数据模型，称为分布式反馈环路网络 (DFNets)，它结合使用一类强健的谱图滤波器 (feedback-looped filters) 并具有更好的定位能力，同时获得较快的收敛速度和线性内存需求；实验结果表明，该模型在半监督的文档分类和知识图谱实体分类两个基准任务上都显著优于现有的方法。

DFNets: 具反馈回路滤波器的图卷积神经网络