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 | Conference PDF | Archive PDF | Plain Text | 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. |