Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SmartCity.2015.68 | 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity) |
Keywords | Field | DocType |
Weighted Networks,Low-Cost Sparse Recovery,Compressive Sensing,Social Networks | Computer science,Inference,Sparse approximation,Signal recovery,Measurement uncertainty,Theoretical computer science,Sparse matrix,Compressed sensing,Binary number | Conference |
Citations | PageRank | References |
3 | 0.38 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hamidreza Mahyar | 1 | 33 | 4.58 |
| Hamid R. Rabiee | 2 | 336 | 41.77 |
| Ali Movaghar | 3 | 683 | 71.03 |
| Rouzbeh Hasheminezhad | 4 | 3 | 0.71 |
| Elaheh Ghalebi | 5 | 8 | 0.79 |
| Ali Nazemian | 6 | 8 | 0.79 |