Graph Random Neural Networks for Semi-Supervised Learning on Graphs
Authors: Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on graph benchmark datasets suggest that GRAND significantly outperforms state-of-the-art GNN baselines on semi-supervised node classification. Finally, we show that GRAND mitigates the issues of over-smoothing and non-robustness, exhibiting better generalization behavior than existing GNNs. The source code of GRAND is publicly available at https://github.com/Grand20/grand. |
| Researcher Affiliation | Collaboration | Wenzheng Feng1 , Jie Zhang2 , Yuxiao Dong3, Yu Han1, Huanbo Luan1, Qian Xu2, Qiang Yang2, Evgeny Kharlamov4, Jie Tang1 1 Department of Computer Science and Technology, Tsinghua University 2We Bank Co., Ltd 3Microsoft Research 4 Bosch Center for Artificial Intelligence |
| Pseudocode | Yes | Algorithm 1 GRAND |
| Open Source Code | Yes | The source code of GRAND is publicly available at https://github.com/Grand20/grand. |
| Open Datasets | Yes | We conduct experiments on three benchmark graphs [42, 20, 35] Cora, Citeseer, and Pubmed and also report results on six publicly available and large datasets in Appendix C.1. |
| Dataset Splits | Yes | We follow exactly the same experimental procedure such as features and data splits as the standard GNN settings on semi-supervised graph learning [42, 20, 35]. The setup and reproducibility details are covered in Appendix A. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models) used for running its experiments. |
| Software Dependencies | Yes | All experiments were implemented with Python 3.6 and PyTorch 1.4.0. |
| Experiment Setup | Yes | For all datasets, we set the propagation step K=2. We train the model using Adam optimizer with initial learning rate 0.001 and weight decay 0.0005. The batch size is 128. For Drop Node and dropout, the drop rate δ is set to 0.5. For consistency regularization, we set λ to 1 and the temperature T to 0.5. The number of augmentations S is 10. |