基于训练和测试时间增长的图神经网络社会影响预测
Social Influence Prediction with Train and Test Time Augmentation for Graph Neural Networks
摘要：
数据增强已广泛用于机器学习中的自然语言处理和计算机视觉任务，以提高模型性能。然而，很少有研究研究了图神经网络的数据增强，特别是在训练和测试时使用增强。受其他领域增强成功的启发，我们设计了一种使用具有训练和测试时间增强的图神经网络进行社会影响力预测的方法，该方法可以通过在两种情况下利用变分图自动编码器有效地为社交网络生成多个增强图。我们已经评估了我们的方法在预测用户对多个社交网络数据集的影响方面的性能。我们的实验结果表明，我们的端到端的方法，联合训练图自动编码器和社会影响力行为分类网络，可以优于国家的最先进的方法，证明了训练和测试时间增加图神经网络的社会影响力预测的有效性。我们观察到，这对较小的图特别有效。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
