Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks

Franco Manessi; Alessandro Rozza

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arXiv · 2011.07267

Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks

Franco Manessi·Alessandro Rozza

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Abstract

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.

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BibTeX RIS

Franco Manessi, Alessandro Rozza. 2020-11-14. Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks. https://arxiv.org/abs/2011.07267

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