Inductive Graph Neural Networks for Spatiotemporal Kriging
Authors: Yuankai Wu, Dingyi Zhuang, Aurelie Labbe, Lijun Sun4478-4485
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on several real-world spatiotemporal datasets demonstrate the effectiveness of our model. |
| Researcher Affiliation | Academia | 1Mc Gill University, Montreal, Canada 2HEC Montreal, Montreal, Canada |
| Pseudocode | Yes | Algorithm 1 Subgraph signal and random mask generation |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | METR-LA is a traffic speed dataset from 207 sensors in Los Angeles over four months (Mar 1, 2012 to Jun 30, 2012);... The two datasets [METR-LA and Pe MS-Bay] are used side by side in (Li et al. 2017). |
| Dataset Splits | Yes | For IGNNK, we take data from the first 70% of the time points as a training set X and test the kriging performance on the following 30% of the time points. ... We use the first 70% of the time points from unsampled nodes as a validation set to select the optimal hyperparameters, and evaluate recovery performance on the following 30% of the time points. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers used for the experiments. |
| Experiment Setup | Yes | We choose h = 24 (i.e., 2 h) for the three traffic datasets, h = 16 (i.e., 80 min) for NREL, and h = 6 (i.e., 6 months) for USHCN. We perform kriging using a sliding-window approach. |