Inferring directed network topologies via tensor factorization

Yanning Shen, Brian Baingana, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Directed networks are pervasive both in nature and engineered systems, often underlying the complex behavior observed in biological systems, microblogs and social interactions over the web, as well as global financial markets. Since their explicit structures are often unobservable, in order to facilitate network analytics, one generally resorts to approaches capitalizing on measurable nodal processes to infer the unknown topology. Prominent among these are structural equation models (SEMs), capable of incorporating exogenous inputs to resolve inherent directional ambiguities. However, this assumes full knowledge of exogenous inputs, which may not be readily available in some practical settings. The present paper advocates a novel SEM-based topology inference approach that entails a PARAFAC decomposition of a three-way tensor, constructed from the observed nodal data. It turns out that second-order statistics of exogenous variables suffice to identify the hidden topology. Leveraging the uniqueness properties inherent to high-order tensor factorizations, it is shown that topology identification is possible under reasonably mild conditions. Tests on simulated data corroborate the effectiveness of the novel tensor-based approach.

Original languageEnglish (US)
Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538639542
StatePublished - Mar 1 2017
Event50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States
Duration: Nov 6 2016Nov 9 2016

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/TerritoryUnited States
CityPacific Grove

Bibliographical note

Publisher Copyright:
© 2016 IEEE.


  • CANDE-COMP/PARAFAC (CP) decomposition
  • Structural equation models
  • network topology inference


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