# HyperJoin: LLM-augmented Hypergraph Link Prediction for Joinable Table Discovery

arXiv: https://arxiv.org/abs/2601.01015 · HTML: https://mlsanity.com/paper/2601.01015/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 48 references that are in the map.
- First posted on arXiv 3 Jan 2026.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning](https://mlsanity.com/paper/2210.01922/) (2022)
- [Large Language Models as Data Preprocessors](https://mlsanity.com/paper/2308.16361/) (2023)
- [Dataset Discovery in Data Lakes](https://mlsanity.com/paper/2011.10427/) (2020)
- [LSH Ensemble: Internet-Scale Domain Search](https://mlsanity.com/paper/1603.07410/) (2016)
- [Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach](https://mlsanity.com/paper/2010.13273/) (2020)
- [On LLM-Enhanced Mixed-Type Data Imputation with High-Order Message Passing](https://mlsanity.com/paper/2501.02191/) (2025)
- [Classification in biological networks with hypergraphlet kernels](https://mlsanity.com/paper/1703.04823/) (2017)
- [Pylon: Semantic Table Union Search in Data Lakes](https://mlsanity.com/paper/2301.04901/) (2023)
- [Efficient Unsupervised Community Search with Pre-trained Graph Transformer](https://mlsanity.com/paper/2403.18869/) (2024)
- [Neural Attributed Community Search at Billion Scale](https://mlsanity.com/paper/2403.18874/) (2024)
- [Simpler is More: Efficient Top-K Nearest Neighbors Search on Large Road Networks](https://mlsanity.com/paper/2408.05432/) (2024)
- [SilkMoth: An Efficient Method for Finding Related Sets with Maximum Matching Constraints](https://mlsanity.com/paper/1704.04738/) (2017)
- [Auto-Prep: Holistic Prediction of Data Preparation Steps for Self-Service Business Intelligence](https://mlsanity.com/paper/2504.11627/) (2025)
- [Snoopy: Effective and Efficient Semantic Join Discovery via Proxy Columns](https://mlsanity.com/paper/2502.16813/) (2025)

## Built on by (most cited first, 2 of 2)

- [Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs](https://mlsanity.com/paper/2601.17058/) (2026)
- [Interpretable Column Annotation with LLM-Symbolized Decision Process Materialization](https://mlsanity.com/paper/2607.25228/) (2026)

## Abstract (arXiv)

As a pivotal task in data lake management, joinable table discovery has attracted widespread interest. While existing language model-based methods achieve remarkable performance by combining offline column representation learning with online ranking, their design insufficiently accounts for the underlying structural interactions: (1) offline, they directly model tables into isolated or pairwise columns, thereby struggling to capture the rich inter-table and intra-table structural information; and (2) online, they rank candidate columns based solely on query-candidate similarity, ignoring the mutual interactions among the candidates, leading to incoherent result sets. To address these limitations, we propose HyperJoin, a large language model (LLM)-augmented Hypergraph framework for Joinable table discovery. Specifically, we first construct a hypergraph to model tables using both the intra-table hyperedges and the LLM-augmented inter-table hyperedges. Consequently, the task of joinable table discovery is formulated as link prediction on this constructed hypergraph. We then design HIN, a Hierarchical Interaction Network that learns expressive column representations through bidirectional message passing over columns and hyperedges. To strengthen coherence and internal consistency in the result columns, we cast online ranking as a coherence-aware top-k column selection problem. We then introduce a reranking module that leverages a maximum spanning tree algorithm to prune noisy connections and maximize coherence. Experiments demonstrate the superiority of HyperJoin, achieving average improvements of 21.4% (Precision@15) and 17.2% (Recall@15) over the best baseline.