Unitary Convolutions for Learning on Graphs and Groups
Authors: Bobak Kiani, Lukas Fesser, Melanie Weber
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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: bkiani@g.harvard.edu John A. Paulson School of Engineering and Applied Sciences, Harvard; e-mail: lukas_fesser@fas.harvard.edu John A. Paulson School of Engineering and Applied Sciences, Harvard; e-mail: mweber@g.harvard.edu |
| 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. |