# GNN: Graph Neural Network and Large Language Model for Data Discovery

arXiv: https://arxiv.org/abs/2408.13609 · HTML: https://mlsanity.com/paper/2408.13609/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 24 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [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)
- [METAM: Goal-Oriented Data Discovery](https://mlsanity.com/paper/2304.09068/) (2023)
- [BOD: Blindly Optimal Data Discovery](https://mlsanity.com/paper/2401.05712/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)

## Abstract (arXiv)

Our algorithm GNN: Graph Neural Network and Large Language Model for Data Discovery inherit the benefits of citehoang2024plod (PLOD: Predictive Learning Optimal Data Discovery), citeHoang2024BODBO (BOD: Blindly Optimal Data Discovery) in terms of overcoming the challenges of having to predefine utility function and the human input for attribute ranking, which helps prevent the time-consuming loop process. In addition to these previous works, our algorithm GNN leverages the advantages of graph neural networks and large language models to understand text type values that cannot be understood by PLOD and MOD, thus making the task of predicting outcomes more reliable. GNN could be seen as an extension of PLOD in terms of understanding the text type value and the user's preferences, not only numerical values but also text values, making the promise of data science and analytics purposes.