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..
Understanding Oversquashing in GNNs through the Lens of Effective Resistance
Authors: Mitchell Black, Zhengchao Wan, Amir Nayyeri, Yusu Wang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5, we empirically demonstrate that our rewiring technique is effective in alleviating oversquashing. and 5. Experiments |
| Researcher Affiliation | Academia | 1School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon, USA 2Halıcıoˇglu Data Science Institute, University of California San Diego, San Diego, California, USA. |
| Pseudocode | No | The paper describes the 'Greedy Total Resistance (GTR) rewiring' heuristic in paragraph form within Section 4 but does not provide a structured pseudocode or algorithm block. |
| Open Source Code | Yes | A Py Torch Geometric implementation of the GTR algorithm is available online2. and footnote 2: https://github.com/blackmit/gtr_rewiring |
| Open Datasets | Yes | All datasets are from the TUDataset (Morris et al., 2020). |
| Dataset Splits | Yes | We use randomly generated 80%/10%/10% train/validation/test splits of the data. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. It does not mention any hardware specifications. |
| Software Dependencies | No | The paper mentions 'Py Torch Geometric' and 'Torch' but does not provide specific version numbers for these software dependencies or any other ancillary software. |
| Experiment Setup | Yes | Table 4: Hyperparameters for Graph Classifcation. These are consistent across all GNN types. These are the same as used in the experiments in (Karhadkar et2022) Hyperparameters Number of Hidden Layers 4 Dimension of Hidden Layers 64 Dropout 0.5 Learning Rate 1.0 10 3 |