# TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction

arXiv: https://arxiv.org/abs/2108.09127 · HTML: https://mlsanity.com/paper/2108.09127/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 20 Aug 2021.

## Builds on (most cited first, 18 of 18)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1905.06874/) (2019)
- [Representation Learning for Attributed Multiplex Heterogeneous Network](https://mlsanity.com/paper/1905.01669/) (2019)
- [AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://mlsanity.com/paper/2003.11235/) (2020)
- [Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures](https://mlsanity.com/paper/2011.07457/) (2020)
- [Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data](https://mlsanity.com/paper/1807.00311/) (2018)
- [AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications](https://mlsanity.com/paper/1904.12857/) (2019)
- [Abstract Diagrammatic Reasoning with Multiplex Graph Networks](https://mlsanity.com/paper/2006.11197/) (2020)
- [Multi-dimensional Graph Convolutional Networks](https://mlsanity.com/paper/1808.06099/) (2018)
- [MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2008.05673/) (2020)

## Built on by (most cited first, 26 of 26)

- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [TransTab: Learning Transferable Tabular Transformers Across Tables](https://mlsanity.com/paper/2205.09328/) (2022)
- [Griffin: Towards a Graph-Centric Relational Database Foundation Model](https://mlsanity.com/paper/2505.05568/) (2025)
- [SwitchTab: Switched Autoencoders Are Effective Tabular Learners](https://mlsanity.com/paper/2401.02013/) (2024)
- [Representation Learning for Tabular Data: A Comprehensive Survey](https://mlsanity.com/paper/2504.16109/) (2025)
- [Hopular: Modern Hopfield Networks for Tabular Data](https://mlsanity.com/paper/2206.00664/) (2022)
- [A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data](https://mlsanity.com/paper/2407.02112/) (2024)
- [Relational In-Context Learning via Synthetic Pre-training with Structural Prior](https://mlsanity.com/paper/2603.03805/) (2026)
- [Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions](https://mlsanity.com/paper/2401.02143/) (2024)
- [Graph Neural Network contextual embedding for Deep Learning on Tabular Data](https://mlsanity.com/paper/2303.06455/) (2023)
- [AutoG: Towards automatic graph construction from tabular data](https://mlsanity.com/paper/2501.15282/) (2025)
- [Learning Enhanced Representations for Tabular Data via Neighborhood Propagation](https://mlsanity.com/paper/2206.06587/) (2022)
- [MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fields](https://mlsanity.com/paper/2302.02978/) (2023)
- [From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNs](https://mlsanity.com/paper/2506.02243/) (2025)
- [Interpretable Graph Neural Networks for Tabular Data](https://mlsanity.com/paper/2308.08945/) (2023)
- [TabGSL: Graph Structure Learning for Tabular Data Prediction](https://mlsanity.com/paper/2305.15843/) (2023)
- [Novel Representation Learning Technique using Graphs for Performance Analytics](https://mlsanity.com/paper/2401.10799/) (2024)
- [TabGLM: Tabular Graph Language Model for Learning Transferable Representations Through Multi-Modal Consistency Minimization](https://mlsanity.com/paper/2502.18847/) (2025)
- [Between-Sample Relationship in Learning Tabular Data Using Graph and Attention Networks](https://mlsanity.com/paper/2306.06772/) (2023)
- [AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine](https://mlsanity.com/paper/2311.14304/) (2023)
- [Interpretable Graph Neural Networks for Heterogeneous Tabular Data](https://mlsanity.com/paper/2408.07661/) (2024)
- [Geodesic Flow Kernels for Semi-Supervised Learning on Mixed-Variable Tabular Dataset](https://mlsanity.com/paper/2412.12864/) (2024)
- [IVGAE: Handling Incomplete Heterogeneous Data with a Variational Graph Autoencoder](https://mlsanity.com/paper/2511.22116/) (2025)
- [Can Graphs Improve Tabular Foundation Models?](https://mlsanity.com/paper/2512.12405/) (2025)
- [A Roadmap for Applying Graph Neural Networks to Numerical Data: Insights from Cementitious Materials](https://mlsanity.com/paper/2512.14855/) (2025)
- [TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning](https://mlsanity.com/paper/2606.11640/) (2026)

## Abstract (arXiv)

Tabular data prediction (TDP) is one of the most popular industrial applications, and various methods have been designed to improve the prediction performance. However, existing works mainly focus on feature interactions and ignore sample relations, e.g., users with the same education level might have a similar ability to repay the debt. In this work, by explicitly and systematically modeling sample relations, we propose a novel framework TabGNN based on recently popular graph neural networks (GNN). Specifically, we firstly construct a multiplex graph to model the multifaceted sample relations, and then design a multiplex graph neural network to learn enhanced representation for each sample. To integrate TabGNN with the tabular solution in our company, we concatenate the learned embeddings and the original ones, which are then fed to prediction models inside the solution. Experiments on eleven TDP datasets from various domains, including classification and regression ones, show that TabGNN can consistently improve the performance compared to the tabular solution AutoFE in 4Paradigm.