Title: Variational Bayesian Complex Network Reconstruction,
Abstract: Complex network reconstruction is a hot topic in many fields. A popular
data-driven reconstruction framework is based on lasso. However, it is found
that, in the presence of noise, it may be inefficient for lasso to determine
the network topology. 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 method to infer the unknown coefficients. Based on the
obtained information, the network is finally reconstructed by determining
whether two nodes connect with each other or not. The numerical experiments
conducted with both synthetic and real data demonstrate that the new method
outperforms lasso with regard to both reconstruction accuracy and running
speed.