Topology inference of multilayer networks

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

3 Scopus citations

Abstract

Linear structural equation models (SEMs) have been very successful in identifying the topology of complex graphs, such as those representing tactical, social and brain networks. The rising popularity of multilayer networks, presents the need for tools that are tailored to leverage the layered structure of the underlying network. To this end, a multilayer SEM is put forth, to infer causal relations between nodes belonging to multilayer networks. An efficient algorithm based on the alternating direction method of multipliers (ADMM) is developed, and preliminary tests on synthetic as well as real data demonstrate the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publication2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages898-903
Number of pages6
ISBN (Electronic)9781538627846
DOIs
StatePublished - Nov 20 2017
Event2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017 - Atlanta, United States
Duration: May 1 2017May 4 2017

Publication series

Name2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017

Other

Other2017 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2017
Country/TerritoryUnited States
CityAtlanta
Period5/1/175/4/17

Keywords

  • Multilayer networks
  • Structural Equation Models
  • Topology inference

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