Conditional Structure Generation through Graph Variational Generative Adversarial Nets
Authors: Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two deliberately created benchmark datasets of real-world context-enriched networks demonstrate the supreme effectiveness and generalizability of CONDGEN. |
| Researcher Affiliation | Academia | Carl Yang , Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li University of Illinois at Urbana Champaign, Urbana, IL 61801 {jiyang3, peiye, wenhans2, alanluu2, panli2}@illinois.edu |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | All code and data used in our experiments have been made available on Git Hub3. 3https://github.com/Kelest Z/Cond Gen |
| Open Datasets | Yes | All code and data used in our experiments have been made available on Git Hub3. 3https://github.com/Kelest Z/Cond Gen. DBLP source: https://dblp.uni-trier.de/ TCGA source: https://www.cancer.gov/tcga |
| Dataset Splits | No | We firstly partition all networks at random by a ratio of 1:1 into training and testing sets. No explicit mention of a validation set split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch versions). |
| Experiment Setup | No | Due to space limit, we put detailed parameter settings, qualitative visual inspections and in-depth model analyses into the appendix in the supplemental materials. |