Network reliability analysis based on percolation theory

Daqing Li, Qiong Zhang, Enrico Zio, Shlomo Havlin, Rui Kang

Research output: Contribution to journalArticlepeer-review

105 Scopus citations


Abstract In this paper, we propose a new way of looking at the reliability of a network using percolation theory. In this new view, a network failure can be regarded as a percolation process and the critical threshold of percolation can be used as network failure criterion linked to the operational settings under control. To demonstrate our approach, we consider both random network models and real networks with different nodes and/or edges lifetime distributions. We study numerically and theoretically the network reliability and find that the network reliability can be solved as a voting system with threshold given by percolation theory. Then we find that the average lifetime of random network increases linearly with the average lifetime of its nodes with uniform life distributions. Furthermore, the average lifetime of the network becomes saturated when system size is increased. Finally, we demonstrate our method on the transmission network system of IEEE 14 bus.

Original languageEnglish
Article number5330
Pages (from-to)556-562
Number of pages7
JournalReliability Engineering and System Safety
StatePublished - 13 Jul 2015

Bibliographical note

Funding Information:
This work is supported by National Natural Science Foundation of China , China (Grant no. 61104144 ). Enrico Zio would thank the project Laboratoire Internationale Associé 2MCSI: Modelling of aging components for system reliability analysis and risk assessment. S. Havlin acknowledges support from the Israel Science Foundation , Israel and the European Projects LINC (No. 289447 ) and MULTIPLEX (No. 317532 ).

Publisher Copyright:
© 2015 The Authors.


  • Criticality
  • Network reliability
  • Percolation theory
  • Phase transition
  • Random network


Dive into the research topics of 'Network reliability analysis based on percolation theory'. Together they form a unique fingerprint.

Cite this