Unsupervised spectral clustering for hierarchical modelling and criticality analysis of complex networks

Abstract : Infrastructure networks are essential to the socioeconomic development of any country. This article applies clustering analysis to extract the inherent structural properties of realistic-size infrastructure networks. Network components with high criticality are identified and a general hierarchical modelling framework is developed for representing the networked system into a scalable hierarchical structure of corresponding fictitious networks. This representation makes a multi-scale criticality analysis possible, beyond the widely used component-level criticality analysis, whose results obtained from zoom-in analysis can support confident decision making.
Document type :
Journal articles
Complete list of metadatas

Cited literature [52 references]  Display  Hide  Download

https://hal-supelec.archives-ouvertes.fr/hal-00838315
Contributor : Yanfu Li <>
Submitted on : Tuesday, June 25, 2013 - 11:50:29 AM
Last modification on : Thursday, June 13, 2019 - 12:08:10 PM

File

1-s2.0-S0951832013000562-main....
Explicit agreement for this submission

Identifiers

Citation

Yi-Ping Fang, Enrico Zio. Unsupervised spectral clustering for hierarchical modelling and criticality analysis of complex networks. Reliability Engineering and System Safety, Elsevier, 2013, 116, pp.64-74. ⟨10.1016/j.ress.2013.02.021⟩. ⟨hal-00838315⟩

Share

Metrics

Record views

280

Files downloads

423