Handling Missing Data with Graph Representation Learning
Authors: Jiaxuan You, Xiaobai Ma, Yi Ding, Mykel J. Kochenderfer, Jure Leskovec
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on nine benchmark datasets show that GRAPE yields 20% lower mean absolute error for imputation tasks and 10% lower for label prediction tasks, compared with existing state-of-the-art methods. |
| Researcher Affiliation | Academia | Jiaxuan You1 Xiaobai Ma2 Daisy Yi Ding3 Mykel Kochenderfer2 Jure Leskovec1 1Department of Computer Science, 2Department of Aeronautics and Astronautics, and 3Department of Biomedical Data Science, Stanford University {jiaxuan, jure}@cs.stanford.edu {maxiaoba, dingd, mykel}@stanford.edu |
| Pseudocode | Yes | Algorithm 1 GRAPE forward computation |
| Open Source Code | Yes | 1Project website with data and code: http://snap.stanford.edu/grape |
| Open Datasets | Yes | We conduct experiments on 9 datasets from the UCI Machine Learning Repository [1]. |
| Dataset Splits | Yes | We randomly split the labels Y into 70/30% training and test sets, Ytrain and Ytest respectively. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU models, CPU types, or memory specifications. It mentions running '5 trials' but not on what hardware. |
| Software Dependencies | No | The paper mentions 'Adam optimizer [28]', 'RELU activation', and 'MLP' but does not specify any software libraries or frameworks with version numbers (e.g., TensorFlow, PyTorch, scikit-learn versions). |
| Experiment Setup | Yes | For all experiments, we train GRAPE for 20,000 epochs using the Adam optimizer [28] with a learning rate at 0.001. For all feature imputation tasks, we use a 3-layer GNN with 64 hidden units and RELU activation. The AGGl is implemented as a mean pooling function MEAN( ) and Oedge as a multi-layer perceptron (MLP) with 64 hidden units. For label prediction tasks, we use two GNN layers with 16 hidden units. Oedge and Onode are implemented as linear layers. The edge dropout rate is set to rdrop = 0.3. |