Graph Neural Machine: A New Model for Learning with Tabular Data
Giannis Nikolentzos Siyun Wang Johannes Lutzeyer Michalis Vazirgiannis
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.