Inferring failure coupling strength in complex networks through generative models

Yimeng Liu, Mingyang Bai, Shaobo Sui, Shiyan Liu, Orr Levy, Jihong Li, Rui Peng, Daqing Li

Research output: Contribution to journalArticlepeer-review

Abstract

The failure propagation process has always been a research hotspot in the field of complex system reliability. Most of the research studies focus on the inference of network structure, ignoring one of the most critical parameters for network reliability, failure coupling strength. Here, we develop a generative model describing the failure process to infer the failure coupling strength. Using this model, we generate the node failure sequences under different coupling strength and analyze the spatial properties of failure propagation. We find the failed nodes tend to appear near the previously failed nodes with increasing coupling strength. Based on our generative model, we propose a Bayesian inference method to infer the failure coupling strength from observed system instantaneous failure state data. We find the inferred values are close to the true values for small failure coupling strength. As failure coupling strength increases, error comes from the limits of failure propagation spatial data. We apply this Bayesian inference method to actual biological networks and infer the network failure coupling strength. Our proposed Bayesian inference method helps analyze the hidden failure mechanism from actual scenarios.

Original languageEnglish
JournalInternational Journal of General Systems
DOIs
StateAccepted/In press - 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

  • complex network
  • Failure propagation process
  • generative model
  • inference

Fingerprint

Dive into the research topics of 'Inferring failure coupling strength in complex networks through generative models'. Together they form a unique fingerprint.

Cite this