Understanding Oversquashing in GNNs through the Lens of Effective Resistance
Authors: Mitchell Black, Zhengchao Wan, Amir Nayyeri, Yusu Wang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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 |