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Probabilistic disintegration for spatial network
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AI
DOI：10.1016/j.ress.2025.111880.png)
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Abstract
En 中文
Spatial networks consist of nodes and edges embedded in geographic space, where failures can propagate outward from a localized center due to natural hazards or targeted attacks. The purpose of this study is to develop a modeling approach that more accurately captures how failure probability decreases with distance, thereby enabling a more accurate assessment of the resilience of spatial networks. Traditional disintegration models often assume the complete removal of all elements within a fixed region and neglect gradual decay in failure likelihood. To address this, we propose a probabilistic framework that integrates geographic embedding into failure modeling through five distance-dependent decay functions, ranging from simple linear to composite exponential and Gaussian forms, thereby representing both sharply localized and more diffuse disruptions. We also introduce a virtual node model that discretizes only the segment of each edge within a disintegration circle into a sequence of representative virtual nodes, avoiding computationally expensive geometric intersection checks. An edge fails if any of its virtual nodes fail. Experiments on several real-world spatial networks demonstrate that deterministic removal produces abrupt connectivity thresholds, whereas probabilistic decay yields smoother degradation patterns. The proposed framework enables realistic, efficient, and flexible modeling of spatial network disintegration for resilience analysis.
Journal
R
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11
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9.0K
Citations:
4.2W