From the 1 of 4 linked papers with an AI index.
4 papers
PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models
Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik +4
The paper presents PLUREL, a lightweight framework for generating synthetic multi-table relational databases, enabling the study of scaling laws in relational foundation models and…
Relational Graph Transformer
Vijay Prakash Dwivedi, Sri Jaladi, Yangyi Shen +5
Relational Deep Learning (RDL) is a promising approach for building state-of-the-art predictive models on multi-table relational data by representing it as a heterogeneous temporal…
Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures
Vijay Prakash Dwivedi, Charilaos Kanatsoulis, Shenyang Huang +1
Graph machine learning has led to a significant increase in the capabilities of models that learn on arbitrary graph-structured data and has been applied to molecules, social netwo…
Large Language Models are Good Relational Learners
Fang Wu, Vijay Prakash Dwivedi, Jure Leskovec
Large language models (LLMs) have demonstrated remarkable capabilities across various domains, yet their application to relational deep learning (RDL) remains underexplored. Existi…