The explainability of deep networks is becoming a central issue in the deep learning community. It is the same for learning on graphs, a data structure present in many real world problems. In this paper, we propose a method that is more optimal, lighter, consistent and better exploits the topology of the evaluated graph than the state-of-the-art methods.
翻译:深层网络的可解释性正在成为深层学习界的一个中心问题。 在图表上学习也是如此。 图表是许多现实世界问题中存在的数据结构。 在本文中,我们提出了一种比最先进的方法更优化、更轻、更一致和更好的方法。