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A low-cost sparse recovery framework for weighted networks under compressive sensing

Mahyar, H ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1109/SmartCity.2015.68
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2015
  4. Abstract:
  5. 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
  6. Keywords:
  7. Low-Cost Sparse Recovery ; Social Networks ; Weighted Networks ; Big data ; Compressed sensing ; Computer system recovery ; Costs ; Human computer interaction ; Recovery ; Signal reconstruction ; Social networking (online) ; Extensive simulations ; Real-world networks ; Sparse recovery ; Sparse signal recoveries ; State-of-the-art methods ; Topological constraints ; Distributed computer systems
  8. Source: Proceedings - 2015 IEEE International Conference on Smart City, SmartCity 2015, Held Jointly with 8th IEEE International Conference on Social Computing and Networking, SocialCom 2015, 5th IEEE International Conference on Sustainable Computing and Communications, SustainCom 2015, 2015 International Conference on Big Data Intelligence and Computing, DataCom 2015, 5th International Symposium on Cloud and Service Computing, SC2 2015, 19 December 2015 through 21 December 2015 ; 2015 , Pages 183-190 ; 9781509018932 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/7463722