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.
A graphon-signal analysis of graph neural networks
Authors: Ron Levie
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 42,518 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,306 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 | 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. |