  Complex network reconstruction is a hot topic in many fields. Currently, the
most popular data-driven reconstruction framework is based on lasso. However,
it is found that, in the presence of noise, lasso loses efficiency for weighted
networks. This paper builds a new framework to cope with this problem. The key
idea is to employ a series of linear regression problems to model the
relationship between network nodes, and then to use an efficient variational
Bayesian algorithm to infer the unknown coefficients. The numerical experiments
conducted on both synthetic and real data demonstrate that the new method
outperforms lasso with regard to both reconstruction accuracy and running
speed.
