[Paper Review] Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions

Cheng–Te Li, Yu‐Che Tsai+2 more|arXiv (Cornell University)|Jan 4, 2024

Advanced Graph Neural Networks4 citations

TL;DR

This survey presents a comprehensive taxonomy and systematic review of Graph Neural Networks (GNNs) for Tabular Data Learning (GNN4TDL), addressing key challenges such as modeling latent correlations in tabular data. It details graph construction, representation learning, training strategies, and auxiliary tasks, offering a roadmap for future research in this emerging field with practical applications across diverse domains.

ABSTRACT

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.

Motivation & Objective

• Address the critical gap in deep learning-based Tabular Data Learning (TDL) where latent correlations among data instances and feature values are undermodeled.
• Systematically investigate the design and implementation of GNNs for TDL, focusing on graph structure construction and representation learning.
• Provide a comprehensive taxonomy of GNN4TDL methods, emphasizing graph formulation, structure learning, and training plans.
• Examine the integration of auxiliary tasks and self-supervised learning to enhance instance representation learning in tabular GNNs.
• Identify limitations in current GNN4TDL research and propose actionable future research directions to advance the field.

Proposed method

• Propose a structured taxonomy for GNN4TDL, categorizing methods based on graph construction strategies and representation learning techniques.
• Review existing GNN-based TDL methods, analyzing their architectural choices and performance across classification and regression tasks.
• Introduce and evaluate a range of self-supervised learning (SSL) tasks tailored for tabular data, including missing feature imputation, graph clustering, and denoising.
• Analyze training strategies that incorporate auxiliary tasks to improve node representation quality and generalization in GNNs for tabular data.
• Investigate robustness issues such as noisy graph structures, data distribution shifts, overfitting, oversmoothing, and adversarial attacks in GNN4TDL.
• Use empirical analysis and case studies to demonstrate the effectiveness of GNN4TDL across diverse applications, including recommendation systems, anomaly detection, and time series analysis.

Experimental results

Research questions

• RQ1 How do GNN-based TDL methods differ fundamentally from conventional deep learning approaches in modeling tabular data?
• RQ2 What are the most effective strategies for constructing graph structures from tabular data under varying TDL scenarios and tasks?
• RQ3 What principles govern effective representation learning in GNNs for tabular data, particularly in capturing high-order feature interactions and instance relationships?
• RQ4 Which TDL tasks and application domains benefit most from GNN integration, and why?
• RQ5 What are the key limitations of current GNN4TDL methods, and what future research directions can address them?

Key findings

• GNNs significantly improve performance in tabular data learning by modeling high-order instance-feature relationships and latent correlations that traditional deep learning models often miss.
• Self-supervised learning tasks such as missing feature imputation and graph denoising enhance representation learning and improve downstream prediction accuracy.
• Graph structure learning and auxiliary tasks like contrastive learning and neighborhood prediction lead to more robust and generalizable node embeddings in tabular GNNs.
• Robustness issues such as oversmoothing, overfitting, and adversarial attacks remain critical challenges, especially in small or noisy tabular datasets.
• The integration of GNNs into TDL has demonstrated strong performance across diverse domains, including recommender systems, anomaly detection, and time series analysis.
• Despite promising results, current GNN4TDL methods lack systematic evaluation of graph construction strategies and robust training plans, highlighting a need for standardized benchmarks and methodologies.