  Graph Convolutional Networks (GCNs) have proven to be successful tools for
semi-supervised learning on graph-based datasets. For sparse graphs, linear and
polynomial filter functions have yielded impressive results. For large
non-sparse graphs, however, network training and evaluation becomes
prohibitively expensive. By introducing low-rank filters, we gain significant
runtime acceleration and simultaneously improved accuracy. We further propose
an architecture change mimicking techniques from Model Order Reduction in what
we call a reduced-order GCN. Moreover, we present how our method can also be
applied to hypergraph datasets and how hypergraph convolution can be
implemented efficiently.
