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..
Unitary Convolutions for Learning on Graphs and Groups
Authors: Bobak Kiani, Lukas Fesser, Melanie Weber
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results here show that unitary/orthogonal variants of graph convolutional networks perform competitively on various graph learning tasks. |
| Researcher Affiliation | Academia | John A. Paulson School of Engineering and Applied Sciences, Harvard; e-mail: EMAIL John A. Paulson School of Engineering and Applied Sciences, Harvard; e-mail: EMAIL John A. Paulson School of Engineering and Applied Sciences, Harvard; e-mail: EMAIL |
| Pseudocode | Yes | Algorithm 1 Unitary map from Lie algebra... Algorithm 3 Unitary map in Fourier basis |
| Open Source Code | Yes | Code available at https://github.com/Weber-GeoML/Unitary_Convolutions |
| Open Datasets | Yes | Long Range Graph Benchmark (LRGB) [DRG+22]... Heterophilous Graph Dataset proposed by [PKD+23]... TU Dataset database [MKB+20]... LRGB [DRG+22] Custom See here for license TUDataset [MKB+20] Open Open sourced here Heterophily Data [PKD+23] N/A Data is open source; no license stated in repository |
| Dataset Splits | Yes | For a given GNN model, we train on a part of the dataset and evaluate performance on a withheld test set using a train/val/test split of 50/25/25 percent. |
| Hardware Specification | Yes | All our experiments were trained on a single GPU (we used either Nvidia Tesla V100 or Nvidia RTX A6000 GPUs). |
| Software Dependencies | No | Experiments were run on Pytorch [PGC+17] and specifically the Pytorch Geometric package for training GNNs [FL19]. The paper mentions the software but not specific version numbers. |
| Experiment Setup | Yes | Training procedures and hyperparameters are reported in App. G. Reported results in tables are over the mean plus/minus standard deviation. |