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