A Low-Cost Sparse Recovery Framework for Weighted Networks under Compressive Sensing

In this paper, motivated by network inference, we introduce a general framework, called LSR-Weighted, to efficiently recover sparse characteristic of links in weighted networks. The links in many real-world networks are not only binary entities, either present or not, but rather have associated weights that record their strengths relative to one another. Such models are generally described in terms of weighted networks. The LSR-Weighted framework uses a newly emerged paradigm in sparse signal recovery named compressive sensing. We study the problem of recovering sparse link vectors with network topological constraints over weighted networks. We evaluate performance of the proposed framework on real-world networks of various kinds, in comparison with two of the state-of-the-art methods for this problem. Extensive simulation results illustrate that our method outperforms the previous methods in terms of recovery error for different number of measurements with relatively low cost.