Position: Relational Deep Learning - Graph Representation Learning on Relational Databases
No confident code implementation found for this paper.
No confident code implementation found for this paper.
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
MDDM: Practical Message-Driven Generative Image Steganography Based on Diffusion Models
FLAT: Flux-Aware Imperceptible Adversarial Attacks on 3D Point Clouds
Common Canvas: Open Diffusion Models Trained on Creative-Commons Images
Riemannian SAM: Sharpness-Aware Minimization on Riemannian Manifolds
Hard-Meta-Dataset++: Towards Understanding Few-Shot Performance on Difficult Tasks
Interpretable Sparse System Identification: Beyond Recent Deep Learning Techniques on Time-Series Prediction
@article{fey2026position,
title = {Position: Relational Deep Learning - Graph Representation Learning on Relational Databases},
author = {Matthias Fey and Weihua Hu and Kexin Huang and J. E. Lenssen and Rishabh Ranjan and Joshua Robinson and Rex Ying and Jiaxuan You and J. Leskovec},
year = {2026},
url = {https://api.semanticscholar.org/CorpusID:1181c36f7b932384defbc97c9b5d792618fda1a5},
journal = {ICML 2024 2024}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/1181c36f7b932384defbc97c9b5d792618fda1a5)