GLAD: Improving Latent Graph Generative Modeling with Simple Quantization
Authors: Van Khoa Nguyen, Yoann Boget, Frantzeska Lavda, Alexandros Kalousis
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | 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. |