  Recently, graph neural networks (GNNs) have shown prominent performance in
graph representation learning by leveraging knowledge from both graph structure
and node features. However, most of them have two major limitations. First,
GNNs can learn higher-order structural information by stacking more layers but
can not deal with large depth due to the over-smoothing issue. Second, it is
not easy to apply these methods on large graphs due to the expensive
computation cost and high memory usage. In this paper, we present node-adaptive
feature smoothing (NAFS), a simple non-parametric method that constructs node
representations without parameter learning. NAFS first extracts the features of
each node with its neighbors of different hops by feature smoothing, and then
adaptively combines the smoothed features. Besides, the constructed node
representation can further be enhanced by the ensemble of smoothed features
extracted via different smoothing strategies. We conduct experiments on four
benchmark datasets on two different application scenarios: node clustering and
link prediction. Remarkably, NAFS with feature ensemble outperforms the
state-of-the-art GNNs on these tasks and mitigates the aforementioned two
limitations of most learning-based GNN counterparts.
