Robust network structure reconstruction based on Bayesian compressive sensing

Abstract

Complex network has proven to be a general model to characterize interactions of practical complex systems. Recently, reconstructing the structure of complex networks with limited and noisy data attracts much research attention and has gradually become a hotspot. However, the collected data are often contaminated by unknown outliers inevitably, which seriously affects the accuracy of network reconstruction. Unfortunately, the existence of outliers is hard to be identified and always ignored in the network structure reconstruction task. To address this issue, here we propose a novel method which involves the outliers from the Bayesian perspective. The accuracy and the robustness of the proposed method have been verified in network reconstruction with payoff data contaminated with some outliers on both artificial networks and empirical networks. Extensive simulation results demonstrate the superiority of the proposed method. Thus, it can be concluded that since the proposed method can identify and get rid of outliers in observation data, it is conducive to improve the performance of network reconstruction.