Smart City Gnosys

Smart city article details

Title	Dngnn: Efficient Deep Noisy Graph Neural Network For Spatio-Temporal Series Forecasting
ID_Doc	20803
Authors	Wang S.; Chen Y.; Xu H.; Hu J.; Zeng P.; Hu Z.
Year	2025
Published	2025 2nd International Conference on Algorithms, Software Engineering and Network Security, ASENS 2025
DOI	http://dx.doi.org/10.1109/ASENS64990.2025.11011208
Abstract	Spatio-temporal series forecasting plays a vital role in the domain of multivariate time series prediction. By leveraging accurate and comprehensive spatio-temporal sequence prediction models, it becomes possible to extract higher-order spatiotemporal interactions between real-world connections, thereby contributing to smart city construction. Spatio-Temporal Graph Neural Networks (STGNNs) have emerged as a highly effective approach for addressing spatio-temporal series prediction challenges. Nevertheless, most existing methods predominantly rely on traditional graph convolutional networks (GCNs) and their variants for spatial feature extraction. Due to inherent limitations such as over-smoothing, these models often constrain spatial feature encoding to shallow architectures, typically limited to 2-3 layers. Additionally, the presence of noisy data in real-world scenarios significantly impacts the prediction accuracy of existing STGNN models. In this paper, building upon the classic STGNN framework, we propose a graph neural network framework based on deep residual connections and noise injection mechanism to address the aforementioned issues. Specifically, inspired by the principles of initial residual connection and identity mapping, we proposed a deep spatio-temporal graph neural network framework of 12 layers to capture the high-dimensional interactions, while using noise injection mechanism to mitigate the adverse effects of noisy data. Sufficient experimental results on typical time-series datasets demonstrate the effectiveness of our proposed framework. © 2025 IEEE.
Author Keywords	Graph Neural Network; Residual Connection; Spatio-temporal Sequence Prediction; Transformer

Similar Articles

Id	Similarity	Authors	Title	Published
52567	0.925	Jin G.; Liang Y.; Fang Y.; Shao Z.; Huang J.; Zhang J.; Zheng Y.	Spatio-Temporal Graph Neural Networks For Predictive Learning In Urban Computing: A Survey	IEEE Transactions on Knowledge and Data Engineering, 36, 10 (2024)
59603	0.919	Li Y.; Lin Y.; Gao Y.; Khan L.	Unified Spatio-Temporal Graph Neural Networks: Data-Driven Modeling For Social Science	Proceedings of the International Joint Conference on Neural Networks, 2022-July (2022)
37180	0.904	Liang Y.; Ouyang K.; Wang Y.; Pan Z.; Yin Y.; Chen H.; Zhang J.; Zheng Y.; Rosenblum D.S.; Zimmermann R.	Mixed-Order Relation-Aware Recurrent Neural Networks For Spatio-Temporal Forecasting	IEEE Transactions on Knowledge and Data Engineering, 35, 9 (2023)
52568	0.899	Ren H.; Kang J.; Zhang K.	Spatio-Temporal Graph-Tcn Neural Network For Traffic Flow Prediction	2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2022 (2022)
52572	0.893	Tang J.; Qian T.; Liu S.; Du S.; Hu J.; Li T.	Spatio-Temporal Latent Graph Structure Learning For Traffic Forecasting	Proceedings of the International Joint Conference on Neural Networks, 2022-July (2022)
53042	0.893	Jin Z.; Xu J.; Huang R.; Shao W.; Xiao X.	Stm2Cn: A Multi-Graph Attention-Based Framework For Sensor Data Prediction In Smart Cities	Proceedings of the International Joint Conference on Neural Networks, 2022-July (2022)
6155	0.893	Jin X.-B.; Ma H.; Xie J.-Y.; Kong J.; Deveci M.; Kadry S.	Ada-Stgmat: An Adaptive Spatio-Temporal Graph Multi-Attention Network For Intelligent Time Series Forecasting In Smart Cities	Expert Systems with Applications, 269 (2025)
23809	0.89	Ouyang X.; Li Y.; Guo D.; Huang W.; Yang X.; Yang Y.; Zhang J.; Li T.	Enhancing Few-Sample Spatio-Temporal Prediction Via Relational Fusion-Based Hypergraph Neural Network	Information Fusion, 121 (2025)
38415	0.889	Zhao W.; Zhang S.; Zhou B.; Wang B.	Multi-Spatio-Temporal Fusion Graph Recurrent Network For Traffic Forecasting	Engineering Applications of Artificial Intelligence, 124 (2023)
52564	0.889	Zhang N.; Li K.; Zhang X.; Wang W.; Li C.; Kang Y.	Spatio-Temporal Graph Network-Based Traffic Forecasting Method	2024 4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024 (2024)