We demonstrate a deep learning framework which is inherently based in the highly expressive language of relational logic, enabling to, among other things, capture arbitrarily complex graph structures. We show how Graph Neural Networks and similar models can be easily covered in the framework by specifying the underlying propagation rules in the relational logic. The declarative nature of the used language then allows to easily modify and extend the propagation schemes into complex structures, such as the molecular rings which we choose for a short demonstration in this paper.

中文翻译：

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学习图神经网络之外的分子

我们展示了一个深度学习框架，它本质上基于关系逻辑的高度表达语言，能够捕捉任意复杂的图结构。我们展示了如何通过在关系逻辑中指定底层传播规则，在框架中轻松覆盖图神经网络和类似模型。所用语言的声明性质允许轻松修改传播方案并将其扩展到复杂的结构中，例如我们选择在本文中进行简短演示的分子环。