Understanding Dropout for Graph Neural Networks

Abstract

Graph neural network (GNN) has demonstrated superior performance on graph learning tasks. GNN captures the data dependencies via message passing amid neural networks. Hence the prediction of a node label can utilize information from its neighbors in a graph. Dropout is a regularization as well as an ensemble method for convolutional neural network (CNN), which has been carefully studied. However, there are few existing works that focused on dropout schemes for GNN. Although GNN and CNN share similar model architecture, both with convolutional layers and fully connected layers, the input data structure for GNN and CNN are different and convolution operation differs. This suggests the dropout schemes for CNN should not be directly applied to GNN without a good understanding of the impact. In this paper, we divide the existing dropout schemes for GNN into two categories: (1) dropout on feature maps and (2) dropout on graph structure. Based on the drawbacks of current GNN dropout models, we propose a novel layer compensation dropout and a novel adaptive heteroscadestic Gaussian dropout, which can be applied to any type of GNN models and outperforms their corresponding baselines in shallow GNNs. Then an experimental study shows Bernoulli dropout generalize better while Gaussian dropout is slightly stronger in transductive performance. At last, we theoretically study how different dropout schemes mitigate over-smoothing problems and experimental results shows that layer compensation dropout allows a GNN model to maintain or slightly improve its performance as the GNN model adds more layers while all the other dropout models suffer from performance degradation when GNN goes deep.