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..
Local Augmentation for Graph Neural Networks
Authors: Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and analyses show that local augmentation consistently yields performance improvement when applied to various GNN architectures across a diverse set of benchmarks. |
| Researcher Affiliation | Collaboration | Songtao Liu 1 Rex Ying 2 Hanze Dong 3 Lanqing Li 4 Tingyang Xu 4 Yu Rong 4 Peilin Zhao 4 Junzhou Huang 4 Dinghao Wu 1 1The Pennsylvania State University 2Stanford University 3Hong Kong University of Science and Technology 4Tencent AI Lab. |
| Pseudocode | Yes | Algorithm 1 Local Augmentation for Graph Neural Networks |
| Open Source Code | Yes | Code is available at https://github.com/ Songtao Liu0823/LAGNN. |
| Open Datasets | Yes | We utilize three public citation network datasets Cora, Citeseer, and Pubmed (Sen et al., 2008) for semi-supervised node classification. All the dataset statistics can be found in Appendix D. |
| Dataset Splits | Yes | We apply the standard fixed splits (Yang et al., 2016) on Cora, Citeseer, and Pubmed, with 20 nodes per class for training, 500 nodes for validation, and 1,000 nodes for testing. |
| Hardware Specification | Yes | All the experiments in this work are conducted on a single NVIDIA Tesla V100 with 32GB memory size. |
| Software Dependencies | Yes | The software that we use for experiments are Python 3.6.8, pytorch 1.9.0, pytorch-cluster 1.5.9, pytorch-scatter 2.0.9, pytorch-sparse 0.6.12, pyg 2.0.3, ogb 1.3.2, dgl 0.7.2, numpy 1.19.2, torchvision 0.10.0, CUDA 10.2.89, and CUDNN 7.6.5. |
| Experiment Setup | Yes | More details about hyparatemeters can be found in Table 10 and 11. |