  Graph Neural Networks (GNNs) have achieved remarkable performance on
graph-based tasks. The key idea for GNNs is to obtain informative
representation through aggregating information from local neighborhoods.
However, it remains an open question whether the neighborhood information is
adequately aggregated for learning representations of nodes with few neighbors.
To address this, we propose a simple and efficient data augmentation strategy,
local augmentation, to learn the distribution of the node features of the
neighbors conditioned on the central node's feature and enhance GNN's
expressive power with generated features. Local augmentation is a general
framework that can be applied to any GNN model in a plug-and-play manner. It
samples feature vectors associated with each node from the learned conditional
distribution as additional input for the backbone model at each training
iteration. Extensive experiments and analyses show that local augmentation
consistently yields performance improvement when applied to various GNN
architectures across a diverse set of benchmarks. For example, experiments show
that plugging in local augmentation to GCN and GAT improves by an average of
3.4\% and 1.6\% in terms of test accuracy on Cora, Citeseer, and Pubmed.
Besides, our experimental results on large graphs (OGB) show that our model
consistently improves performance over backbones. Code is available at
https://github.com/SongtaoLiu0823/LAGNN.
