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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A graphon-signal analysis of graph neural networks
Authors: Ron Levie
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Table 2 in Appendix G.5 we present experiments that illustrate the generalization capabilities of MPNNs with normalized sum aggregation. |
| Researcher Affiliation | Academia | Ron Levie Faculty of Mathematics Technion Israel Institute of Technology EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code, courtesy of Ningyuan (Teresa) Huang, is available as part of https://github. com/nhuang37/finegrain_expressivity_GNN . |
| Open Datasets | Yes | Table 2 lists datasets such as MUTAG, IMDB-BINARY, IMDB-MULTI, NCI1, PROTEINS, and REDDIT-BINARY, which are standard public datasets used in graph machine learning. |
| Dataset Splits | No | The paper mentions 'mean accuracy std over ten data splits' but does not specify the percentages or sample counts for training, validation, and test splits, nor does it provide a specific methodology for these splits beyond mentioning 'train' and 'test' in Table 2. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions 'PyTorch Geometric' in reference [11] but does not specify version numbers for any software components used in their own implementation. |
| Experiment Setup | Yes | Table 2 states '3-layers with 512-hidden-dimension, and global mean pooling', which are specific details of the experimental setup and model architecture. |