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Entropy-driven sensor deployment and source detection in hypergraphs
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DOI：10.1016/j.eswa.2026.134058.png)
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Abstract
En 中文
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The framework SSDH addresses sensor-based source detection in hypergraphs.
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Our deployment strategy maximizes coverage under limited sensor resources.
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The algorithm combines path uncertainty and topological distance for accuracy.
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SSDH outperforms baselines by 5-30% on synthetic and empirical datasets.
Abstract
Identifying diffusion origins is fundamental to controlling the spread of epidemics and misinformation. Existing sensor-based methods, however, are largely developed for simple networks and are not well suited to higher-order spreading processes. We propose Sensor-based Source Detection in Hypergraphs (SSDH), a framework that integrates sensor deployment with source detection in hypergraph-structured systems. SSDH deploys sensors by maximizing incremental information gain and locates diffusion sources using a path-entropy measure that combines transmission uncertainty with structural proximity. Experiments on hypergraphs ranging from several hundred to more than 5000 nodes show that SSDH consistently outperforms existing methods. Under sensor budgets of 5%–15%, it improves detection accuracy by 5%–30% while reducing detection error. Even with only 10% sensor coverage, SSDH frequently surpasses competing methods operating with twice the sensing budget, indicating that reliable source detection remains possible under highly constrained observations.
Keywords:
Sensor deployment
Source detection
Hypergraph
Information entropy
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Citations:
10.2W
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