  Data augmentation has been widely used in machine learning for natural
language processing and computer vision tasks to improve model performance.
However, little research has studied data augmentation on graph neural
networks, particularly using augmentation at both train- and test-time.
Inspired by the success of augmentation in other domains, we have designed a
method for social influence prediction using graph neural networks with train-
and test-time augmentation, which can effectively generate multiple augmented
graphs for social networks by utilising a variational graph autoencoder in both
scenarios. We have evaluated the performance of our method on predicting user
influence on multiple social network datasets. Our experimental results show
that our end-to-end approach, which jointly trains a graph autoencoder and
social influence behaviour classification network, can outperform
state-of-the-art approaches, demonstrating the effectiveness of train- and
test-time augmentation on graph neural networks for social influence
prediction. We observe that this is particularly effective on smaller graphs.
