Active Sensing of Social Networks

Hoi To Wai, Anna Scaglione, Amir Leshem

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


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 languageAmerican English
Article number7456339
Pages (from-to)406-419
Number of pages14
JournalIEEE Transactions on Signal and Information Processing over Networks
Issue number3
StatePublished - Sep 2016
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2015 IEEE.


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


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