Identifiability of sparse structural equation models for directed and cyclic networks

Juan Andres Bazerque, Brian Baingana, Georgios B. Giannakis

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

17 Scopus citations

Abstract

Structural equation models (SEMs) provide a statistical description of directed networks. The networks modeled by SEMs may have signed edge weights, a property that is pertinent to represent the activating and inhibitory interactions characteristic of biological systems, as well as the collaborative and antagonist behaviors found in social networks, among other applications. They may also have cyclic paths, accommodating the presence of protein stabilizing loops, or the feedback in decision making processes. Starting from the mathematical description of a linear SEM, this paper aims to identify the topology, edge directions, and edge weights of the underlying network. It is established that perturbation data is essential for this purpose, otherwise directional ambiguities cannot be resolved. It is also proved that the required amount of data is significantly reduced when the network topology is assumed to be sparse; that is, when the number of incoming edges per node is much smaller than the network size. Identifying a dynamic network with step changes across time is also considered, but it is left as an open problem to be addressed in an extended version of this paper.

Original languageEnglish (US)
Title of host publication2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages839-842
Number of pages4
DOIs
StatePublished - 2013
Event2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States
Duration: Dec 3 2013Dec 5 2013

Publication series

Name2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Other

Other2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
CountryUnited States
CityAustin, TX
Period12/3/1312/5/13

Keywords

  • Directed networks
  • Identifiability
  • Kruskal rank
  • Sparsity
  • Structural equation models

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