  Graph Convolutional Networks (GCNs) and their variants have achieved
significant performances on various recommendation tasks. However, many
existing GCN models tend to perform recursive aggregations among all related
nodes, which can arise severe computational burden to hinder their application
to large-scale recommendation tasks. To this end, this paper proposes the
flattened GCN~(FlatGCN) model, which is able to achieve superior performance
with remarkably less complexity compared with existing models. Our main
contribution is three-fold. First, we propose a simplified but powerful GCN
architecture which aggregates the neighborhood information using one flattened
GCN layer, instead of recursively. The aggregation step in FlatGCN is
parameter-free such that it can be pre-computed with parallel computation to
save memory and computational cost. Second, we propose an informative
neighbor-infomax sampling method to select the most valuable neighbors by
measuring the correlation among neighboring nodes based on a principled metric.
Third, we propose a layer ensemble technique which improves the expressiveness
of the learned representations by assembling the layer-wise neighborhood
representations at the final layer. Extensive experiments on three datasets
verify that our proposed model outperforms existing GCN models considerably and
yields up to a few orders of magnitude speedup in training efficiency.
