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

Abstract

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.

arXiv commentAccepted by The International Joint Conference on Neural Networks 2021 (IJCNN 2021)

References in corpus (9)

• Auto-Encoding Variational Bayes
• Semi-Supervised Classification with Graph Convolutional Networks
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• Empirical Evaluation of Rectified Activations in Convolutional Network
• Unsupervised Data Augmentation for Consistency Training
• DeepInf: Social Influence Prediction with Deep Learning
• TrueTop: A Sybil-Resilient System for User Influence Measurement on Twitter
• Augment your batch: better training with larger batches
• Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning