Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
Authors: Ziqi Gao, Yifan Niu, Jiashun Cheng, Jianheng Tang, Lanqing Li, Tingyang Xu, Peilin Zhao, Fugee Tsung, Jia Li
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that MEGAE outperforms all the other state-of-the-art imputation methods on a variety of benchmark datasets. |
| Researcher Affiliation | Collaboration | Ziqi Gao2, Yifan Niu1, Jiashun Cheng2, Jianheng Tang2, Lanqing Li3*, Tingyang Xu3, Peilin Zhao3, Fugee Tsung1,2, Jia Li1,2 1The Hong Kong University of Science and Technology (Guangzhou) 2The Hong Kong University of Science and Technology 3AI Lab, Tencent |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | No statement or link provided regarding open-source code for the described methodology. |
| Open Datasets | Yes | We conduct experiments on 6 benchmark datasets (Morris et al. 2020) from different domains: (1) bioinformatics, i.e., PROTEINS_full (Borgwardt et al. 2005) and ENZYMES (Schomburg et al. 2004); (2) chemistry, i.e., QM9 (Ramakrishnan et al. 2014) and FIRSTMM_DB (Neumann et al. 2013); (3) computer vision, i.e., FRANKENSTEIN (Orsini, Frasconi, and De Raedt 2015); (4) synthesis, i.e., Synthie (Morris et al. 2016). |
| Dataset Splits | Yes | We use a 70-10-20 train-validation-test split and construct random missingness only on the test set. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU/CPU models, cloud instances) used for the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., library or solver names with specific versions). |
| Experiment Setup | No | The paper states 'For all baselines, we use a 2-layer GCN for downstream classification' and mentions a '70-10-20 train-validation-test split' and 'After running for 5 trials', but it does not provide specific hyperparameters like learning rate, batch size, number of epochs, or optimizer settings in the main text. |