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.

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.