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..
Inductive Graph Neural Networks for Spatiotemporal Kriging
Authors: Yuankai Wu, Dingyi Zhuang, Aurelie Labbe, Lijun Sun4478-4485
AAAI 2021 | Venue PDF | 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. |