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

Social Influence Prediction with Train and Test Time Augmentation for Graph Neural Networks