We demonstrate a declarative differentiable programming framework based on the language of Lifted Relational Neural Networks, where small parameterized logic programs are used to encode relational learning scenarios. When presented with relational data, such as various forms of graphs, the program interpreter dynamically unfolds differentiable computational graphs to be used for the program parameter optimization by standard means. Following from the used declarative Datalog abstraction, this results into compact and elegant learning programs, in contrast with the existing procedural approaches operating directly on the computational graph level. We illustrate how this idea can be used for an efficient encoding of a diverse range of existing advanced neural architectures, with a particular focus on Graph Neural Networks (GNNs). Additionally, we show how the contemporary GNN models can be easily extended towards higher relational expressiveness. In the experiments, we demonstrate correctness and computation efficiency through comparison against specialized GNN deep learning frameworks, while shedding some light on the learning performance of existing GNN models.

基于Lifted Relational Neural Networks语言实现了一个说明性可区分编程框架，用于编码关系学习场景，特别关注图神经网络的高关系表达能力。该框架能够快速构建一些高效紧凑的神经网络结构，并提出了一些关于现有图神经网络模型的改进建议。通过与专业GNN学习框架的比较，证明了该框架具有正确性和计算效率，并为现有GNN模型的学习性能提供了一些启示。

基于提升关系神经网络的图神经网络扩展