HO-OTSVD: A Novel Tensor Decomposition and Its Incremental Decomposition for Cyber-Physical-Social Networks (CPSN)

Puming Wang, Laurence T. Yang, Gonwei Qian, Jin Li, Zheng Yan

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The development of social networks and ubiquitous sensing promotes the network space into a new stage, which integrates the cyber network, physical network and social network into Cyber-Physical-Social Networks (CPSN). In this paper, we propose a CPSN-based service framework. The framework firstly represents Cyber-Physical-Social Networks (CPSN) as an adjacency tensor (AT). Then a novel Tensor Decomposition (TD) method named high-order orthogonal tensor singular value decomposition (HO-OTSVD) is proposed for knowledge discovery. To cope with the dynamic CPSN, an incremental HO-OTSVD (IHO-OTSVD) is developed to update the orthogonal tensor basis and the core tensor. Furthermore, we propose high-order bidiagonal Lanczos (HOBL) algorithm to cope with the orthogonalization of HO-OTSVD, the complexity reduces from cubic execution time to quadratic execution time. Lastly, we use a recommendation system as a case study to evaluate the effectiveness and efficiency of the proposed CPSN-based framework. The results show that HO-OTSVD method outperform the existing methods.
Original languageEnglish
Pages (from-to)713-725
Number of pages13
JournalIEEE Transactions on Network Science and Engineering
Volume7
Issue number2
Early online date2019
DOIs
Publication statusPublished - 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Singular value decomposition
  • Social networking (online)
  • Matrix decomposition
  • Computer science
  • Eigenvalues and eigenfunctions
  • Sparse matrices
  • Cyber-Physical-Social Network
  • Tensor
  • Orthogonal tensor basis
  • Eigentensor
  • Recommendation

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