  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.
