Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
Authors: Kiarash Zahirnia, Oliver Schulte, Parmis Naddaf, Ke Li
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that adding micro-macro modeling to the Graph VAE model improves graph quality scores up to 2 orders of magnitude on five benchmark datasets, while maintaining the Graph VAE generation speed advantage. |
| Researcher Affiliation | Academia | Kiarash Zahirnia, Oliver Schulte , Parmis Naddaf, Ke Li School of Computing Science, Simon Fraser University, Canada |
| Pseudocode | No | The paper includes diagrams of the model architecture (Figure 2) but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation and datasets are provided at https: //github.com/kiarashza/Graph VAE-MM. |
| Open Datasets | Yes | We utilize 3 synthetic, and 2 real-world datasets for the main results. The synthetic Grid and Lobster are from previous studies [47, 32], Triangle Grid is introduced in this paper. Protein and ogbg-molbbbp are real-world datasets from biology with information about proteins and molecules respectively. |
| Dataset Splits | Yes | Following previous experiments [47, 30, 11] we randomly split the graphs sets into train (70%), validation (10%) and test (20%) sets. |
| Hardware Specification | No | The paper does not provide specific hardware details in the main body. It refers to Section 8.11 (Appendix) for machine details, which is not provided in this text snippet. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers in the main body. It mentions that library requirements can be found at the provided GitHub repository. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. It states that the Appendix contains further details on experiment settings and hyperparameters. |