English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Spatial network disintegration based on kernel density estimation

Authors
/persons/resource/zhigang.wang

Wang,  Zhigang
Potsdam Institute for Climate Impact Research;

/persons/resource/zhen.su

Su,  Zhen
Potsdam Institute for Climate Impact Research;

Deng,  Ye
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

Wu,  Jun
External Organizations;

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PIKpublic
Supplementary Material (public)
There is no public supplementary material available
Citation

Wang, Z., Su, Z., Deng, Y., Kurths, J., Wu, J. (2024): Spatial network disintegration based on kernel density estimation. - Reliability Engineering & System Safety, 245, 110005.
https://doi.org/10.1016/j.ress.2024.110005


Cite as: https://publications.pik-potsdam.de/pubman/item/item_29681
Abstract
The problem of network disintegration, such as suppression of an epidemic spread and destabilization of terrorist networks, possesses extensive applications and has lately been the focus of growing interest. Many real-world complex systems are represented by spatial networks in which nodes and edges are spatially embedded. However, existing disintegration approaches for spatial network disintegration focus on singular aspects such as geospatial information or network topography, with insufficient modeling granularity. In this paper, we propose an effective and computationally efficient virtual node model that essentially integrates the geospatial information and topology of the network by modeling edges as virtual nodes with weights. Moreover, we employ Kernel Density Estimation, a well-known non-parametric technique for estimating the underlying probability density function of samples, to fit all nodes, comprising both network and virtual nodes, to identify the critical region of the spatial network, which is also the circular geographic region where disintegration occurs. Extensive numerical experiments on synthetic and real-world networks demonstrate that our method outperforms existing methods in terms of both effectiveness and efficiency, which provides a fresh perspective for modeling spatial networks.