Inferring failure coupling strength in complex networks through generative models

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.

KEYWORDS:

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

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data we used supporting the findings presented here can be found at https://github.com/linearworld/Single-Cell-Coexpression-Network.git.

Additional information

Funding

This work is supported by the National Natural Science Foundation of China [grant number 72225012, No. 72288101, No. 71822101], Safety Capability Building Fund of Civil Aviation Administration of China [grant number ASSA2023/19] and the Fundamental Research Funds for the Central Universities.