Notice: 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. Full accuracy metrics and methodology are described in [1].
Differentially Describing Groups of Graphs
Authors: Corinna Coupette, Sebastian Dalleiger, Jilles Vreeken3959-3967
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through an extensive set of experiments on a wide range of synthetic and real-world graph groups, we confirm that GRAGRA works well in practice. |
| Researcher Affiliation | Academia | 1 Max Planck Institute for Informatics 2 CISPA Helmholtz Center for Information Security |
| Pseudocode | Yes | GRAGRA, whose pseudocode is given as Alg. 1 |
| Open Source Code | Yes | All our data, code, and results are publicly available.1 (Footnote 1: https://doi.org/10.5281/zenodo.6342823) |
| Open Datasets | Yes | All our data, code, and results are publicly available.1 (Footnote 1: https://doi.org/10.5281/zenodo.6342823). We obtain graphs from preprocessed functional connectomes provided by the Autism Brain Imaging Data Exchange (Craddock et al. 2013). We obtain data on passenger flows between domestic airports... from the website of the Bureau of Transportation Statistics (Bureau of Transportation Statistics 2021). We obtain data on international trade flows from the website of the World Integrated Trade Solution (World Bank 2021). |
| Dataset Splits | No | The paper does not explicitly describe training, validation, and test splits with specific percentages, counts, or predefined splits for reproduction. |
| Hardware Specification | Yes | We run our experiments on Intel E5-2643 CPUs with 128 or 256 GB RAM |
| Software Dependencies | No | The paper mentions implementing GRAGRA in 'C++' and exposing a 'Python interface' but does not specify version numbers for these or any other software libraries or dependencies. |
| Experiment Setup | No | The paper states 'A detailed overview of our synthetic data configurations can be found in the Online Appendix,' which indicates that experimental setup details are not present in the main text. No specific hyperparameters or training configurations are mentioned for GRAGRA itself in the main body. |