Detecting hidden nodes in networks based on random variable resetting method

Reconstructing network connections from measurable data facilitates our understanding of the mechanism of interactions between nodes. However, the unmeasurable nodes in real networks, also known as hidden nodes, introduce new challenges for reconstruction. There have been some hidden node detection methods, but most of them are limited by system models, network structures, and other conditions. In this paper, we propose a general theoretical method for detecting hidden nodes based on the random variable resetting method. We construct a new time series containing hidden node information based on the reconstruction results of random variable resetting, theoretically analyze the autocovariance of the time series, and finally provide a quantitative criterion for detecting hidden nodes. We numerically simulate our method in discrete and continuous systems and analyze the influence of main factors. The simulation results validate our theoretical derivation and illustrate the robustness of the detection method under different conditions.

Automated summary

Reconstructing network connections aids in understanding node interactions, but hidden nodes pose challenges. This paper proposes a general theoretical method for detecting hidden nodes based on the random variable resetting. A new time series containing hidden node information is constructed, and the autocovariance is analyzed to provide a quantitative criterion. Simulation results confirm the theoretical derivation and demonstrate the method's robustness under different conditions.