Dynamic structural equation models for tracking topologies of social networksy

Brian Baingana, Gonzalo Mateos, Georgios B Giannakis

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

10 Scopus citations

Abstract

Many real-world processes evolve in cascades over complex networks, whose topologies are often unobservable and change over time. However, the so-termed adoption times when blogs mention popular news items, individuals in a community catch an infectious disease, or consumers adopt a trendy electronics product are typically known, and are implicitly dependent on the underlying network. To infer the network topology, a dynamic structural equation model is adopted that captures the relationship between observed adoption times and the unknown edge weights. Assuming a slowly time-varying network and leveraging the sparse connectivity inherent to social networks, edge weights are estimated by minimizing a sparsity-regularized exponentially-weighted least-squares criterion. An alternating-direction method of multipliers solver is developed to this end, and preliminary tests on synthetic network data corroborate the effectiveness of the novel algorithm in unveiling the dynamically-evolving network topology.

Original languageEnglish (US)
Title of host publication2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Pages292-295
Number of pages4
DOIs
StatePublished - Dec 1 2013
Event2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 - Saint Martin, France
Duration: Dec 15 2013Dec 18 2013

Other

Other2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
CountryFrance
CitySaint Martin
Period12/15/1312/18/13

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