Graph-based neural network models with multiple self-supervised auxiliary tasks

摘要

Self-supervised learning is currently gaining a lot of attention, as it allows neural networks to learn ro-bust representations from large quantities of unlabeled data. Additionally, multi-task learning can further improve representation learning by training networks simultaneously on related tasks, leading to signifi-cant performance improvements. In this paper, we propose three novel self-supervised auxiliary tasks to train graph-based neural network models in a multi-task fashion. Since Graph Convolutional Networks are among the most promising approaches for capturing relationships among structured data points, we use them as a building block to achieve competitive results on standard semi-supervised graph classifi-cation tasks. (c) 2021 Elsevier B.V. All rights reserved.