动态图模型提升物联网数据可信度，实时捕捉物理变化与因果关系。
DyC-STG: Dynamic Causal Spatio-Temporal Graph Network for Real-time Data Credibility Analysis in IoT
- 根据事件动态调整图结构，反映真实物理状态变化
- 通过时间先后顺序识别真正因果关系，避免虚假关联
- 适合智能家庭等需实时可信数据的物联网场景
物联网传感器广泛部署产生大量时空数据流，但确保数据可信性仍是智慧家庭等应用中的关键挑战。现有时空图（STG）模型因依赖静态图结构、易混淆虚假相关与真实因果关系，在人机交互环境中表现不佳。为此，我们提出动态因果时空图网络（DyC-STG），用于物联网实时数据可信度分析。该框架包含两个协同模块：事件驱动的动态图模块，可实时更新图拓扑以反映物理状态变化；因果推理模块，通过严格遵循时间先后顺序提取因果感知表征。为推动该领域研究，我们发布两个真实世界数据集。大量实验表明，DyC-STG达到新基准，相比最强基线提升1.4个百分点，最高F1分数达0.930。
原文摘要 · Abstract (English)
The wide spreading of Internet of Things (IoT) sensors generates vast spatio-temporal data streams, but ensuring data credibility is a critical yet unsolved challenge for applications like smart homes. While spatio-temporal graph (STG) models are a leading paradigm for such data, they often fall short in dynamic, human-centric environments due to two fundamental limitations: (1) their reliance on static graph topologies, which fail to capture physical, event-driven dynamics, and (2) their tendency to confuse spurious correlations with true causality, undermining robustness in human-centric environments. To address these gaps, we propose the Dynamic Causal Spatio-Temporal Graph Network (DyC-STG), a novel framework designed for real-time data credibility analysis in IoT. Our framework features two synergistic contributions: an event-driven dynamic graph module that adapts the graph topology in real-time to reflect physical state changes, and a causal reasoning module to distill causally-aware representations by strictly enforcing temporal precedence. To facilitate the research in this domain we release two new real-world datasets. Comprehensive experiments show that DyC-STG establishes a new state-of-the-art, outperforming the strongest baselines by 1.4 percentage points and achieving an F1-Score of up to 0.930.
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