  In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural
Networks (GNNs), a domain where deep learning-based approaches have
increasingly shown superior performance in both classification and regression
tasks compared to traditional methods. The survey highlights a critical gap in
deep neural TDL methods: the underrepresentation of latent correlations among
data instances and feature values. GNNs, with their innate capability to model
intricate relationships and interactions between diverse elements of tabular
data, have garnered significant interest and application across various TDL
domains. Our survey provides a systematic review of the methods involved in
designing and implementing GNNs for TDL (GNN4TDL). It encompasses a detailed
investigation into the foundational aspects and an overview of GNN-based TDL
methods, offering insights into their evolving landscape. We present a
comprehensive taxonomy focused on constructing graph structures and
representation learning within GNN-based TDL methods. In addition, the survey
examines various training plans, emphasizing the integration of auxiliary tasks
to enhance the effectiveness of instance representations. A critical part of
our discussion is dedicated to the practical application of GNNs across a
spectrum of GNN4TDL scenarios, demonstrating their versatility and impact.
Lastly, we discuss the limitations and propose future research directions,
aiming to spur advancements in GNN4TDL. This survey serves as a resource for
researchers and practitioners, offering a thorough understanding of GNNs' role
in revolutionizing TDL and pointing towards future innovations in this
promising area.
