A Simple Spectral Failure Mode for Graph Convolutional Networks

Abstract--We present a simple generative model in which spectral graph embedding for subsequent inference succeeds whereas unsupervised graph convolutional networks (GCN) fail. The geometrical insight is that the GCN is unable to look beyond the first non-informative spectral dimension. 's, we observe a Euclidean space and Email: shenc@udel.edu - Ningyuan (Teresa) Huang and Tianyi Chen are with the Department of's are corrupted through the Bernoulli noise The authors thank Wade Shen for providing the motivation for this investigation. Geometry for the canonical case where ASE succeeds but GCN fails. Figure 1 illustrates the failure mode for GCN.