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 language | English (US) |
|---|---|
| Title of host publication | 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4139-4143 |
| Number of pages | 5 |
| Volume | 2016-May |
| ISBN (Electronic) | 9781479999880 |
| DOIs | |
| State | Published - May 18 2016 |
| Event | 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China Duration: Mar 20 2016 → Mar 25 2016 |
Other
| Other | 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 |
|---|---|
| Country | China |
| City | Shanghai |
| Period | 3/20/16 → 3/25/16 |
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Keywords
- active learning
- online optimization algorithm
- social networks
- system identification
ASJC Scopus subject areas
- Signal Processing
- Software
- Electrical and Electronic Engineering
Cite this
Active online learning of trusts in social networks. / Wai, Hoi To; Scaglione, Anna; Leshem, Amir.
2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Vol. 2016-May Institute of Electrical and Electronics Engineers Inc., 2016. p. 4139-4143 7472456.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Active online learning of trusts in social networks
AU - Wai, Hoi To
AU - Scaglione, Anna
AU - Leshem, Amir
PY - 2016/5/18
Y1 - 2016/5/18
N2 - 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.
AB - 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.
KW - active learning
KW - online optimization algorithm
KW - social networks
KW - system identification
UR - http://www.scopus.com/inward/record.url?scp=84973351897&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84973351897&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2016.7472456
DO - 10.1109/ICASSP.2016.7472456
M3 - Conference contribution
AN - SCOPUS:84973351897
VL - 2016-May
SP - 4139
EP - 4143
BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -