  Self-supervised learning is currently gaining a lot of attention, as it
allows neural networks to learn robust 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 significant 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 classification tasks.
