A graphon-signal analysis of graph neural networks
Authors: Ron Levie
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 levieron@technion.ac.il |
| 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. |