  Graph neural networks (GNNs) are a popular class of parametric model for
learning over graph-structured data. Recent work has argued that GNNs primarily
use the graph for feature smoothing, and have shown competitive results on
benchmark tasks by simply operating on graph-smoothed node features, rather
than using end-to-end learned feature hierarchies that are challenging to scale
to large graphs. In this work, we ask whether these results can be extended to
heterogeneous graphs, which encode multiple types of relationship between
different entities. We propose Neighbor Averaging over Relation Subgraphs
(NARS), which trains a classifier on neighbor-averaged features for
randomly-sampled subgraphs of the "metagraph" of relations. We describe
optimizations to allow these sets of node features to be computed in a
memory-efficient way, both at training and inference time. NARS achieves a new
state of the art accuracy on several benchmark datasets, outperforming more
expensive GNN-based methods
