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..
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. |