In recent years, there has been a growing interest in mapping data from
different domains to graph structures. Among others, neural network models such
as the multi-layer perceptron (MLP) can be modeled as graphs. In fact, MLPs can
be represented as directed acyclic graphs. Graph neural networks (GNNs) have
recently become the standard tool for performing machine learning tasks on
graphs. In this work, we show that an MLP is equivalent to an asynchronous
message passing GNN model which operates on the MLP's graph representation. We
then propose a new machine learning model for tabular data, the so-called Graph
Neural Machine (GNM), which replaces the MLP's directed acyclic graph with a
nearly complete graph and which employs a synchronous message passing scheme.
We show that a single GNM model can simulate multiple MLP models. We evaluate
the proposed model in several classification and regression datasets. In most
cases, the GNM model outperforms the MLP architecture.

近年来，对不同领域数据进行图结构化映射的兴趣日益增长。本文展示了多层感知器（MLP）等神经网络模型可以用图表示，而图神经网络（GNN）是在图上执行机器学习任务的标准工具。我们提出了一种用于表格数据的新的机器学习模型，称为图神经机器（GNM），它用几乎完全图代替了 MLP 的有向无环图，并采用同步消息传递方案。我们展示了单个 GNM 模型可以模拟多个 MLP 模型，并在多个分类和回归数据集上评估了所提出的模型。在大多数情况下，GNM 模型优于 MLP 架构。