  Graph Convolutional Networks (GCNs) and their variants have experienced
significant attention and have become the de facto methods for learning graph
representations. GCNs derive inspiration primarily from recent deep learning
approaches, and as a result, may inherit unnecessary complexity and redundant
computation. In this paper, we reduce this excess complexity through
successively removing nonlinearities and collapsing weight matrices between
consecutive layers. We theoretically analyze the resulting linear model and
show that it corresponds to a fixed low-pass filter followed by a linear
classifier. Notably, our experimental evaluation demonstrates that these
simplifications do not negatively impact accuracy in many downstream
applications. Moreover, the resulting model scales to larger datasets, is
naturally interpretable, and yields up to two orders of magnitude speedup over
FastGCN.
