58ae23d878a47004366189884c2f8440-Supplemental.pdf

Now we look into the term[(A+I)X]TV,:, which is the aggregated feature vectors within neighborhood N1 for nodes in the training set. Note that [(A + I)X]TS,: is a circulant matrix, therefore its inverse exists. Now consider an arbitrary training datapoint(v,yv) TV, and a perturbation added to the neighborhood N(v) of node v, such that the number of nodes with a randomly selected class labelyp Y 6=yv isδ1lessthanexpectedin N(v). Now we move on to discuss the GCN layer formulated asf(X;A,W) = AXW without self loops. We regardcs,i as the coefficient ofs at frequency componenti and regard the coefficients at all frequencies components{cs,i} as the spectrum of signalswith respect to graphG.