Switched dynamic structural equation models for tracking social network topologies

Brian Baingana, Georgios B. Giannakis

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

1 Scopus citations

Abstract

Contagions such as the spread of popular news stories, or infectious diseases, propagate in cascades over dynamic networks with unobservable topologies. However, "social signals" such as product purchase time, or blog entry timestamps are measurable, and implicitly depend on the underlying topology, making it possible to track it over time. Interestingly, network topologies often "jump" between discrete states that may account for sudden changes in the observed signals. The present paper advocates a switched dynamic structural equation model to capture the topology-dependent cascade evolution, as well as the discrete states driving the underlying topologies. Leveraging the edge sparsity inherent to social networks, a recursive ℓ1-norm regularized least-squares estimator is put forth to jointly track the states and network topologies. A first-order proximal-gradient algorithm is developed to solve the resulting optimization problem, and numerical tests on synthetic data corroborate its efficacy.

Original languageEnglish (US)
Title of host publication2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages682-686
Number of pages5
ISBN (Electronic)9781479975914
DOIs
StatePublished - Feb 23 2016
EventIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 - Orlando, United States
Duration: Dec 13 2015Dec 16 2015

Publication series

Name2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

Other

OtherIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
CountryUnited States
CityOrlando
Period12/13/1512/16/15

Keywords

  • Social networks
  • network cascade
  • structural equation model
  • switched linear systems
  • topology inference

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