Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks
This work addresses representation learning for graph-based data, which is incremental as it builds on existing self-supervised and multi-task learning methods.
The paper tackled the problem of learning robust representations from unlabeled graph-structured data by proposing three novel self-supervised auxiliary tasks for multi-task training with Graph Convolutional Networks, achieving competitive results on standard semi-supervised graph classification tasks.
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.