Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Conditional Structure Generation through Graph Variational Generative Adversarial Nets
Authors: Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,744 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,075 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL |
| 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 ο¬rstly 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. |