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.
Diving into Tabular Data Learning (TDL) using Graph Neural Networks (GNNs)
Tabular Data Learning (TDL) is an important domain where deep learning-based approaches have showcased their superiority compared to traditional methods in both classification and regression tasks. However, one critical gap in these deep neural TDL methods has been the underrepresentation of latent correlations among data instances and feature values.
In recent years, Graph Neural Networks (GNNs) have emerged as a solution to this problem. GNNs possess an innate capability to model intricate relationships and interactions between diverse elements of tabular data, making them highly attractive for various TDL domains. This survey aims to provide a comprehensive review of the design and implementation of GNNs for TDL, also known as GNN4TDL.
An Overview of GNN4TDL
This survey extensively explores foundational aspects and offers insights into the evolving landscape of GNN-based TDL methods. It begins by presenting a detailed investigation into constructing graph structures and representation learning within GNNs. This taxonomy allows researchers and practitioners to better understand the different approaches and techniques used in this field.
Additionally, the survey highlights various training plans that enhance the effectiveness of instance representations. One notable strategy is the integration of auxiliary tasks, which can further improve the performance and generalization capabilities of GNNs in TDL.
Practical Applications and Impact of GNNs in TDL
GNNs have demonstrated remarkable versatility and impact across a spectrum of GNN4TDL scenarios. This survey delves into practical applications where GNNs have produced significant results. By showcasing real-world examples, researchers and practitioners gain a deeper understanding of how GNNs can revolutionize TDL in different domains.
Limited Scope and Future Directions
While GNNs show great promise in revolutionizing TDL, this survey acknowledges limitations and proposes directions for future research. By highlighting areas where further advancements are needed, the survey aims to spur innovative research in GNN4TDL.
This survey is a valuable resource for researchers and practitioners seeking a thorough understanding of GNNs’ role in transforming TDL. By exploring the multi-disciplinary nature of the concepts, it bridges the gap between deep learning, graph theory, and TDL. With its comprehensive review and insights, this survey paves the way for future innovations in this promising area.