mlsanityData to 7 Oct 2026

Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions

arXiv 2401.02143 ↗First posted 4 Jan 2024cs.LG cs.AI cs.IRcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 106 of its 177 references that are in the map. First posted on arXiv 4 Jan 2024.

Builds on

106 of 177 references.

References · 106

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38 more
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Citing papers per year: 2024: 2; 2025: 9; 2026: 2.

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  1. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  2. 2025TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Modelscited by 10
  3. 2025AutoG: Towards automatic graph construction from tabular datacited by 6
  4. 2025From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNscited by 4
  5. 2026Grables: Tabular Learning Beyond Independent Rowscited by 2
  6. 2024Graph Neural Machine: A New Model for Learning with Tabular Datacited by 0
  7. 2025Scalable Graph Attention-based Instance Selection via Mini-Batch Sampling and Hierarchical Hashingcited by 0
  8. 2025Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reorderingcited by 0
  9. 2025Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databasescited by 0
  10. 2025Deep Learning for School Dropout Detection: A Comparison of Tabular and Graph-Based Models for Predicting At-Risk Studentscited by 0
  11. 2025The Role of Feature Interactions in Graph-based Tabular Deep Learningcited by 0
  12. 2025A Roadmap for Applying Graph Neural Networks to Numerical Data: Insights from Cementitious Materialscited by 0
1 more
  1. 2026AutoGrable: What Is a Good Graph for a Table?cited by 0