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

• URL: http://arxiv.org/abs/2011.07267v2
• Date: Fri, 4 Dec 2020 14:37:52 GMT
• Title: Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks
• Authors: Franco Manessi, Alessandro Rozza
• Abstract summary: Graph Convolutional Networks are among the most promising approaches for capturing relationships among structured data points. We propose three novel self-supervised auxiliary tasks to train graph-based neural network models in a multi-task fashion.
• Score: 79.28094304325116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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