GN2DI: A Scalable Graph Neural Network Framework for Spatial Missing Data Imputation in Sensor Networks

This paper presents GN2DI, a Graph Neural Network (GNN)-based framework designed for data imputation in sensor networks. GN2DI utilizes a neural graph construction technique that can be optimized for the target task. The proposed method can handle both samples and sensors that were unavailable during the training phase, enabling inductive capabilities for new sensors and samples. GN2DI employs two modules: the first module learns efficient graph representations for the downstream task by following the gradient signal, while the second module imputes the missing values. Various experimental settings were devised to evaluate GN2DI's performance using real-world sensor datasets from different domains. The results show a 2% to 14% improvement over the best benchmark. Additionally, an experiment was conducted to verify the model's performance with new sensors. Also, our ablation study gives insight into the model's performance. The code is available at: https://github.com/AmEskandari/GN2DI.