Identifying susceptible agents in time varying opinion dynamics through compressive measurements

Hoi To Wai, Asuman E. Ozdaglar, Anna Scaglione

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

1 Citation (Scopus)

Abstract

We provide a compressive-measurement based method to detect susceptible agents who may receive misinformation through their contact with 'stubborn agents' whose goal is to influence the opinions of agents in the network. We consider a DeGroot-type opinion dynamics model where regular agents revise their opinions by linearly combining their neighbors' opinions, but stubborn agents, while influencing others, do not change their opinions. Our proposed method hinges on estimating the temporal difference vector of network-wide opinions, computed at time instances when the stubborn agents interact. We show that this temporal difference vector has approximately the same support as the locations of the susceptible agents. Moreover, both the interaction instances and the temporal difference vector can be estimated from a small number of aggregated opinions. The performance of our method is studied both analytically and empirically. We show that the detection error decreases when the social network is better connected, or when the stubborn agents are 'less talkative'.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4114-4118
Number of pages5
Volume2018-April
ISBN (Print)9781538646588
DOIs
StatePublished - Sep 10 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: Apr 15 2018Apr 20 2018

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period4/15/184/20/18

Fingerprint

Error detection
Hinges
Dynamic models

Keywords

  • Compressive sensing
  • Malicious agents
  • Opinion dynamics
  • Spread of misinformation
  • Stubborn agents

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Wai, H. T., Ozdaglar, A. E., & Scaglione, A. (2018). Identifying susceptible agents in time varying opinion dynamics through compressive measurements. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (Vol. 2018-April, pp. 4114-4118). [8462377] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8462377

Identifying susceptible agents in time varying opinion dynamics through compressive measurements. / Wai, Hoi To; Ozdaglar, Asuman E.; Scaglione, Anna.

2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Vol. 2018-April Institute of Electrical and Electronics Engineers Inc., 2018. p. 4114-4118 8462377.

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

Wai, HT, Ozdaglar, AE & Scaglione, A 2018, Identifying susceptible agents in time varying opinion dynamics through compressive measurements. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. vol. 2018-April, 8462377, Institute of Electrical and Electronics Engineers Inc., pp. 4114-4118, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 4/15/18. https://doi.org/10.1109/ICASSP.2018.8462377
Wai HT, Ozdaglar AE, Scaglione A. Identifying susceptible agents in time varying opinion dynamics through compressive measurements. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Vol. 2018-April. Institute of Electrical and Electronics Engineers Inc. 2018. p. 4114-4118. 8462377 https://doi.org/10.1109/ICASSP.2018.8462377
Wai, Hoi To ; Ozdaglar, Asuman E. ; Scaglione, Anna. / Identifying susceptible agents in time varying opinion dynamics through compressive measurements. 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Vol. 2018-April Institute of Electrical and Electronics Engineers Inc., 2018. pp. 4114-4118
@inproceedings{05ee1f2e416f4a3cb1498a9eede88573,
title = "Identifying susceptible agents in time varying opinion dynamics through compressive measurements",
abstract = "We provide a compressive-measurement based method to detect susceptible agents who may receive misinformation through their contact with 'stubborn agents' whose goal is to influence the opinions of agents in the network. We consider a DeGroot-type opinion dynamics model where regular agents revise their opinions by linearly combining their neighbors' opinions, but stubborn agents, while influencing others, do not change their opinions. Our proposed method hinges on estimating the temporal difference vector of network-wide opinions, computed at time instances when the stubborn agents interact. We show that this temporal difference vector has approximately the same support as the locations of the susceptible agents. Moreover, both the interaction instances and the temporal difference vector can be estimated from a small number of aggregated opinions. The performance of our method is studied both analytically and empirically. We show that the detection error decreases when the social network is better connected, or when the stubborn agents are 'less talkative'.",
keywords = "Compressive sensing, Malicious agents, Opinion dynamics, Spread of misinformation, Stubborn agents",
author = "Wai, {Hoi To} and Ozdaglar, {Asuman E.} and Anna Scaglione",
year = "2018",
month = "9",
day = "10",
doi = "10.1109/ICASSP.2018.8462377",
language = "English (US)",
isbn = "9781538646588",
volume = "2018-April",
pages = "4114--4118",
booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Identifying susceptible agents in time varying opinion dynamics through compressive measurements

AU - Wai, Hoi To

AU - Ozdaglar, Asuman E.

AU - Scaglione, Anna

PY - 2018/9/10

Y1 - 2018/9/10

N2 - We provide a compressive-measurement based method to detect susceptible agents who may receive misinformation through their contact with 'stubborn agents' whose goal is to influence the opinions of agents in the network. We consider a DeGroot-type opinion dynamics model where regular agents revise their opinions by linearly combining their neighbors' opinions, but stubborn agents, while influencing others, do not change their opinions. Our proposed method hinges on estimating the temporal difference vector of network-wide opinions, computed at time instances when the stubborn agents interact. We show that this temporal difference vector has approximately the same support as the locations of the susceptible agents. Moreover, both the interaction instances and the temporal difference vector can be estimated from a small number of aggregated opinions. The performance of our method is studied both analytically and empirically. We show that the detection error decreases when the social network is better connected, or when the stubborn agents are 'less talkative'.

AB - We provide a compressive-measurement based method to detect susceptible agents who may receive misinformation through their contact with 'stubborn agents' whose goal is to influence the opinions of agents in the network. We consider a DeGroot-type opinion dynamics model where regular agents revise their opinions by linearly combining their neighbors' opinions, but stubborn agents, while influencing others, do not change their opinions. Our proposed method hinges on estimating the temporal difference vector of network-wide opinions, computed at time instances when the stubborn agents interact. We show that this temporal difference vector has approximately the same support as the locations of the susceptible agents. Moreover, both the interaction instances and the temporal difference vector can be estimated from a small number of aggregated opinions. The performance of our method is studied both analytically and empirically. We show that the detection error decreases when the social network is better connected, or when the stubborn agents are 'less talkative'.

KW - Compressive sensing

KW - Malicious agents

KW - Opinion dynamics

KW - Spread of misinformation

KW - Stubborn agents

UR - http://www.scopus.com/inward/record.url?scp=85054267006&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054267006&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2018.8462377

DO - 10.1109/ICASSP.2018.8462377

M3 - Conference contribution

AN - SCOPUS:85054267006

SN - 9781538646588

VL - 2018-April

SP - 4114

EP - 4118

BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -