20 Citations (Scopus)

Abstract

This paper develops an active sensing method to estimate the relative weight (or trust) agents place on their neighbors' information in a social network. The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents; i.e., agents whose opinions are not influenced by their neighbors. This method can be viewed as a social RADAR, where the stubborn agents excite the system and the latter can be estimated through the reverberation observed from the analysis of the agents' opinions. The social network sensing problem can be interpreted as a blind compressed sensing problem with a sparse measurement matrix. We prove that the network structure will be revealed when a sufficient number of stubborn agents independently influence a number of ordinary (non-stubborn) agents. We investigate the scenario with a deterministic or randomized DeGroot model and propose a consistent estimator of the steady states for the latter scenario. Simulation results on synthetic and real world networks support our findings.

Original languageEnglish (US)
Article number7456339
Pages (from-to)406-419
Number of pages14
JournalIEEE Transactions on Signal and Information Processing over Networks
Volume2
Issue number3
DOIs
StatePublished - Sep 1 2016

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Compressed sensing
Reverberation

Keywords

  • DeGroot model
  • opinion dynamics
  • social networks
  • sparse recovery
  • system identification

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Active Sensing of Social Networks. / Wai, Hoi To; Scaglione, Anna; Leshem, Amir.

In: IEEE Transactions on Signal and Information Processing over Networks, Vol. 2, No. 3, 7456339, 01.09.2016, p. 406-419.

Research output: Contribution to journalArticle

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