  The performance of graph neural nets (GNNs) is known to gradually decrease
with increasing number of layers. This decay is partly attributed to
oversmoothing, where repeated graph convolutions eventually make node
embeddings indistinguishable. We take a closer look at two different
interpretations, aiming to quantify oversmoothing. Our main contribution is
PairNorm, a novel normalization layer that is based on a careful analysis of
the graph convolution operator, which prevents all node embeddings from
becoming too similar. What is more, PairNorm is fast, easy to implement without
any change to network architecture nor any additional parameters, and is
broadly applicable to any GNN. Experiments on real-world graphs demonstrate
that PairNorm makes deeper GCN, GAT, and SGC models more robust against
oversmoothing, and significantly boosts performance for a new problem setting
that benefits from deeper GNNs. Code is available at
https://github.com/LingxiaoShawn/PairNorm.
