A proximal gradient algorithm for tracking cascades over networks

Brian Baingana, Gonzalo Mateos, Georgios B. Giannakis

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

Abstract

Many real-world processes evolve in cascades over networks, whose topologies are often unobservable and change over time. However, the so-termed adoption times when for instance blogs mention popular news items are typically known, and are implicitly dependent on the underlying network. To infer the network topology, a dynamic structural equation model is adopted to capture the relationship between observed adoption times and the unknown edge weights, while accounting also for external (non-topological) perturbations. Assuming a slowly time-varying topology and leveraging the sparse connectivity inherent to social networks, edge weights are estimated by minimizing a sparsity-regularized exponentially-weighted least-squares criterion. To this end, a solver is developed by leveraging (pseudo) real-time sparsity-promoting proximal gradient iterations. Numerical tests with real cascades of online media demonstrate the effectiveness of the novel algorithm in unveiling sparse dynamically-evolving topologies.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4778-4782
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period5/4/145/9/14

Keywords

  • Social network
  • cascade
  • convex optimization
  • structural equation model
  • topology inference

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