GNN: Graph Neural Network and Large Language Model for Data Discovery
Not on the map. Why?
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 37 references that are in the map. First posted on arXiv 24 Aug 2024.
Builds on
7 of 37 references.
References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2023METAM: Goal-Oriented Data Discoverycited by 4
- 2024BOD: Blindly Optimal Data Discoverycited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.
- 2025A Survey of Context Engineering for Large Language Modelscited by 120
- 2025A Survey of Context Engineering for Large Language Models17 Jul 2025