Discrete Graph Auto-Encoder
Authors: Yoann Boget, Magda Gregorova, Alexandros Kalousis
TMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through multiple experimental evaluations, we demonstrate the competitive performances of our model in comparison to the existing state-of-the-art across various datasets. Various ablation studies support the interest of our method. |
| Researcher Affiliation | Academia | Yoann Boget EMAIL Geneva School for Business administration HES-SO University of Geneva Magda Gregorova EMAIL Center for Arti cial Intelligence and Robotics (CAIRO) Technische Hochschule Würzburg-Schweinfurt (THWS) Alexandros Kalousis EMAIL Geneva School for Business administration HES-SO |
| Pseudocode | No | The paper describes methods and processes in detail using natural language and mathematical equations, but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block, nor does it present structured steps in a code-like format. |
| Open Source Code | Yes | The source code of our model is publicly available at https://github.com/yoboget/dgae. |
| Open Datasets | Yes | For simple graphs, we evaluated the performance of our model on two datasets of small graphs, namely Ego-Small and Community-Small, with 200 and 100 graphs, respectively as well as Enzymes, a dataset of larger graphs. We evaluated the performance of our model on two widely used datasets for molecule generation: Qm9 (Ramakrishnan et al., 2014) and Zinc250k (Irwin et al., 2012). |
| Dataset Splits | Yes | In particular, we split the datasets between training and test sets with a ratio of 80% 20% as (Jo et al., 2022) and use the exact same split when available. |
| Hardware Specification | Yes | We compute the clock time to generate 1000 graphs in the rdkit format on one Ge Force RTX 3070 GPU and 12 CPU cores. |
| Software Dependencies | No | The paper mentions software components like 'pytorch-geometric' and 'rdkit' but does not provide specific version numbers for these or any other key software dependencies. |
| Experiment Setup | Yes | Appendix A.3 reports the details of the models, the hyperparameters choices and the resulting number of parameters used for the experiments. In our experiments, we use the p path method setting p = 3. m being the codebook size, is a hyper-parameter. We compare ten con gurations for various codebook size m and number of partition C, representing three sizes of possible codeword sequences M. For Ego-Small and Community-Small, our reported results are the average of fteen runs, i.e., fteen generation batches with the test set size used for evaluation against the test set: three runs from ve models trained independently. For the Enzymes dataset, which contains much larger graphs, we follow (Jo et al., 2022) and average over three runs from a single model. |