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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
What do Graph Neural Networks learn? Insights from Tropical Geometry
Authors: Tuan Anh Pham, Vikas Garg
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 34,257 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,313 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work is purely theoretical and does not involve any experimental result. |
| Researcher Affiliation | Collaboration | Tuan Anh Pham School of Mathematics University of Edinburgh Edinburgh, United Kingdom EMAIL Vikas Garg Yai Yai Ltd and Aalto University EMAIL |
| Pseudocode | Yes | Algorithm 1 Building Φ(t) 2 |
| Open Source Code | No | Our work is purely theoretical and does not any data and code. |
| Open Datasets | No | Our work is purely theoretical and does not involve any experimental result. |
| Dataset Splits | No | Our work is purely theoretical and does not involve any experimental result. |
| Hardware Specification | No | Our work is purely theoretical and does not involve any experimental result. |
| Software Dependencies | No | Our work is purely theoretical and does not involve any experimental result. |
| Experiment Setup | No | Our work is purely theoretical and does not involve any experimental result. |