Variational Bayesian Weighted Complex Network Reconstruction

Shuang Xu; Chun-Xia Zhang; Pei Wang; Jiangshe Zhang

doi:10.1016/j.ins.2020.02.050

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arXiv · 1812.04369

Variational Bayesian Weighted Complex Network Reconstruction

Shuang Xu·Chun-Xia Zhang·Pei Wang·Jiangshe Zhang

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Abstract

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.

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BibTeX RIS

Shuang Xu, Chun-Xia Zhang, Pei Wang, Jiangshe Zhang. 2018-12-11. Variational Bayesian Weighted Complex Network Reconstruction. https://doi.org/10.1016/j.ins.2020.02.050

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