Graph Neural Network-Based Anomaly Detection for Smart Infrastructure Monitoring
Keywords:
graph neural networks, anomaly detection, smart infrastructure, structural health monitoring, network governance, socio-technical systems, machine learning deploymentAbstract
The proliferation of smart infrastructure systems, encompassing energy grids, transportation networks, water distribution systems, and industrial control environments, has generated vast quantities of relational data that are inherently graph-structured. Anomaly detection in such systems is critical for ensuring operational safety, reliability, and resilience, yet traditional statistical and machine learning methods often fail to capture the complex dependencies and non-Euclidean relationships that characterize these networks. Graph neural networks have emerged as a powerful paradigm for learning representations from graph-structured data, enabling sophisticated anomaly detection by modeling both node features and topological context. This paper presents a comprehensive systems-level examination of graph neural network-based anomaly detection for smart infrastructure monitoring, focusing on architectural trade-offs, deployment considerations, governance frameworks, and policy implications. We synthesize recent advances in graph convolutional networks, graph attention networks, and message-passing schemes, and discuss their suitability for detecting structural anomalies, temporal deviations, and attribute-based outliers in large-scale infrastructure graphs. A particular emphasis is placed on the interplay between model expressiveness and computational scalability, the challenges of imbalanced and dynamic graphs, and the need for interpretability and fairness in critical infrastructure domains. We further explore the integration of graph neural network solutions into existing monitoring infrastructures, considering data privacy, regulatory compliance, and human-in-the-loop oversight. Through a series of analytical case illustrations spanning smart grid fault detection and urban traffic anomaly identification, we demonstrate the practical viability and limitations of these methods. The paper concludes with a forward-looking discussion on sustainable model lifecycles, federated learning across infrastructure operators, and the ethical dimensions of automated anomaly response. Our contribution lies in providing a systematic, interdisciplinary roadmap for researchers and practitioners seeking to deploy graph neural network-based anomaly detection systems within real-world smart infrastructure contexts.
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