  The predictive power and overall computational efficiency of
Diffusion-convolutional neural networks make them an attractive choice for node
classification tasks. However, a naive dense-tensor-based implementation of
DCNNs leads to $\mathcal{O}(N^2)$ memory complexity which is prohibitive for
large graphs. In this paper, we introduce a simple method for thresholding
input graphs that provably reduces memory requirements of DCNNs to O(N) (i.e.
linear in the number of nodes in the input) without significantly affecting
predictive performance.
