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..
GraphMix: Improved Training of GNNs for Semi-Supervised Learning
Authors: Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang10024-10032
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate this analysis by applying Graph Mix to various architectures such as Graph Convolutional Networks, Graph Attention Networks and Graph-U-Net. Despite its simplicity, we demonstrate that Graph Mix can consistently improve or closely match stateof-the-art performance using even simpler architectures such as Graph Convolutional Networks, across three established graph benchmarks: Cora, Citeseer and Pubmed citation network datasets, as well as three newly proposed datasets: Cora Full, Co-author-CS and Co-author-Physics. |
| Researcher Affiliation | Academia | 1 Mila Québec Artificial Intelligence Institute, Montréal, Canada, 2 Aalto University, Finland, 3 Massachusetts Institute of Technology (MIT), USA |
| Pseudocode | Yes | A diagram illustrating Graph Mix is presented in Figure 1 and the full algorithm is presented in Appendix A.3. |
| Open Source Code | Yes | Code available at https://github.com/vikasverma1077/Graph Mix |
| Open Datasets | Yes | across three established graph benchmarks: Cora, Citeseer and Pubmed citation network datasets, as well as three newly proposed datasets: Cora Full, Co-author-CS and Co-author-Physics. We use Cora-Full dataset proposed in (Bojchevski and Günnemann 2018) and Coauthor-CS and Coauthor-Physics datasets proposed in (Shchur et al. 2018). |
| Dataset Splits | Yes | Along these lines, we created 10 random splits of the Cora, Citeseer and Pubmed with the same train/ validation/test number of samples as in the standard split. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | No | The paper states 'Refer to Appendix A.8 for implementation and hyperparameter details', deferring the specific experimental setup information to an appendix rather than providing it in the main text. |