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Inductive Graph Neural Networks for Spatiotemporal Kriging (IGNNK)

This is the code corresponding to the experiments conducted for the AAAI 2021 paper “Inductive Graph Neural Networks for Spatiotemporal Kriging” (Yuankai Wu, Dingyi Zhuang, Aurélie Labbe and Lijun Sun).

Motivations

In many applications, placing sensors with fully spatial coverage may be impractical. Installation and maintenance costs of devices can also limit the number of sensors deployed in a network. A better kriging model can achieve higher estimation accuracy/reliability with less number of sensors, thus reducing the operation and maintenance cost of a sensor network. The kriging results can produce a fine-grained and high-resolution realization of