(#83 / Sess. 2) Connecting Graph Convolutional Networks and Graph-Regularized PCA

Graph convolution operator of the GCN model is originally motivated from a localized first-order approximation of spectral graph convolutions. This work stands on a different view; establishing a connection between graph convolution and graph-regularized PCA. Based on this connection, GCN architecture, shaped by stacking graph convolution layers, shares a close relationship with stacking graph-regularized PCA (GPCA). We empirically demonstrate that the unsupervised embeddings by GPCA paired with a logistic regression classifier achieves similar performance to GCN on semi-supervised node classification tasks. Further, we capitalize on the discovered relationship to design an effective initialization strategy for GCN based on stacking GPCA.

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