DARKFARSEER: Inductive Spatio-temporal Kriging via Hidden Style Enhancement and Sparsity-Noise Mitigation

作     者：Liang, Zhuoxuan Li, Wei Zhang, Dalin Chen, Yidan Wang, Zhihong Zheng, Xiangping Youssef, Moustafa 

作者机构：The College of Computer Science and Technology Harbin Engineering University Harbin150001 China The Spatial Information Research Institute Hangzhou Dianzi University Hangzhou310018 China The American University Cairo Egypt 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2025年

核心收录：

主　　题：Graph neural networks 

摘      要：With the rapid growth of the Internet of Things (IoT) and Cyber-Physical Systems (CPS), widespread sensor deployment has become essential. However, the high costs of building sensor networks limit their scale and coverage, making fine-grained deployment challenging. Inductive Spatio-Temporal Kriging (ISK) addresses this issue by introducing virtual sensors. Based on graph neural networks (GNNs) extracting the relationships between physical and virtual sensors, ISK can infer the measurements of virtual sensors from physical sensors. However, current ISK methods rely on conventional message-passing mechanisms and network architectures, without effectively extracting spatio-temporal features of physical sensors and focusing on representing virtual sensors. Additionally, existing graph construction methods face issues of sparse and noisy connections, destroying ISK performance. To address these issues, we propose DARKFARSEER, a novel ISK framework with three key components. First, we propose the Neighbor Hidden Style Enhancement module with a style transfer strategy to enhance the representation of virtual nodes in a temporal-then-spatial manner to better extract the spatial relationships between physical and virtual nodes. Second, we propose Virtual-Component Contrastive Learning, which aims to enrich the node representation by establishing the association between the patterns of virtual nodes and the regional patterns within graph components. Lastly, we design a Similarity-Based Graph Denoising Strategy, which reduces the connectivity strength of noisy connections around virtual nodes and their neighbors based on their temporal information and regional spatial patterns. We examine existing graph construction approaches with preliminary experiments, identify their limitations, and motivate our work. Extensive experiments demonstrate that DARKFARSEER significantly outperforms existing ISK methods, e.g., the improvement in MAE score reaching as high as 10.58%. Copyright