Active online learning of trusts in social networks

Hoi To Wai, Anna Scaglione, Amir Leshem

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

Abstract

This paper considers an online optimization algorithm for actively learning trusts on social networks. We first introduce a DeGroot model for opinion dynamics under the influence of stubborn agents and demonstrate how an observer with estimates of the individuals opinions can actively learn the relative trusts among different agents, by fitting the opinions to the steady state equations of the social system equations. The main contribution of this article is an online algorithm for extracting the trust parameters from streaming data of randomly sampled, noisy opinion estimates. The algorithm is based on the stochastic proximal gradient method and it is proven to converge almost surely. Finally, numerical results are presented to corroborate our findings.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4139-4143
Number of pages5
Volume2016-May
ISBN (Electronic)9781479999880
DOIs
StatePublished - May 18 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: Mar 20 2016Mar 25 2016

Other

Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
CountryChina
CityShanghai
Period3/20/163/25/16

Keywords

  • active learning
  • online optimization algorithm
  • social networks
  • system identification

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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  • Cite this

    Wai, H. T., Scaglione, A., & Leshem, A. (2016). Active online learning of trusts in social networks. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings (Vol. 2016-May, pp. 4139-4143). [7472456] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2016.7472456