Active online learning of trusts in social networks

Hoi-To Wai; Anna Scaglione; Amir Leshem

ICASSP 2016accepted0 citations

Active online learning of trusts in social networks

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.

BibTeX

@inproceedings{icassp2016_activeonlinelear,
  title = {Active online learning of trusts in social networks},
  author = {Hoi-To Wai and Anna Scaglione and Amir Leshem},
  booktitle = {ICASSP 2016},
  year = {2016}
}