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..
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 | Venue PDF | 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. |