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 [1].
Spectral Graph Pruning Against Over-Squashing and Over-Smoothing
Authors: Adarsh Jamadandi, Celia Rubio-Madrigal, Rebekka Burkholz
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments |
| Researcher Affiliation | Academia | 1Universität des Saarlandes 2CISPA Helmholtz Center for Information Security |
| Pseudocode | Yes | Algorithm 1 Proxy Spectral Gap based Greedy Graph Sparsification (PROXYDELETE) |
| Open Source Code | Yes | Our code https://github.com/Relational ML/Spectral Pruning Braess is available. |
| Open Datasets | Yes | We evaluate on the following datasets and tasks: 1) Pascal VOC-SP Semantic image segmentation as a node classification task operating on superpixel graphs. 2) Peptides-func Peptides modeled as molecular graphs. The task is graph classification. 3) Peptides-struct Peptides modeled as molecular graphs. |
| Dataset Splits | Yes | We use a 60/20/20 split for training/testing/validation respectively for all datasets. |
| Hardware Specification | Yes | All experiments were done on 2 V100 GPUs. |
| Software Dependencies | No | The paper mentions using "Py Torch Geometric and DGL library" but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We set the learning rate to {3e 3, 3e 4}, dropout to 0.32, and the hidden dimension size to 512. For GATs, the attention heads are set to 8. |