Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
GLAD: Improving Latent Graph Generative Modeling with Simple Quantization
Authors: Van Khoa Nguyen, Yoann Boget, Frantzeska Lavda, Alexandros Kalousis
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experiments on a series of graph benchmark datasets that demonstrates GLAD as the first equivariant latent graph generative method achieves competitive performance with the state of the art baselines. |
| Researcher Affiliation | Academia | Geneva School for Business Administration (HES-SO) University of Geneva, 1214 Geneva, Switzerland EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Graph Discrete Latent Diffusion Bridge |
| Open Source Code | Yes | Code https://github.com/v18nguye/GLAD |
| Open Datasets | Yes | We measure GLAD s ability to capture the underlying structures of generic graphs on three datasets: (a) egosmall (Sen et al. 2008), (b) community-small, and (c) enzymes (Schomburg et al. 2004). We conduct experiments on two standard datasets: QM9 (Ramakrishnan et al. 2014) and ZINC250k (Irwin et al. 2012). |
| Dataset Splits | Yes | We use the same train- and test-splits as the baselines for a fair comparison. |
| Hardware Specification | No | The computations were performed at the University of Geneva on Baobab and Yggdrasil HPC clusters. |
| Software Dependencies | No | Following (Jo, Lee, and Hwang 2022), we remove hydrogen atoms and kekulize molecules by RDKit Landrum et al. (2016). |
| Experiment Setup | No | Algorithm 1 outlines the training procedure, which includes 'Adam-optim' for optimization, but specific hyperparameters like learning rate, batch size, or number of epochs are not provided in the main text. |