Abstract
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 language | English (US) |
|---|---|
| Article number | 7456339 |
| Pages (from-to) | 406-419 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Signal and Information Processing over Networks |
| Volume | 2 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 1 2016 |
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Keywords
- DeGroot model
- opinion dynamics
- social networks
- sparse recovery
- system identification
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing
Cite this
Active Sensing of Social Networks. / Wai, Hoi To; Scaglione, Anna; Leshem, Amir.
In: IEEE Transactions on Signal and Information Processing over Networks, Vol. 2, No. 3, 7456339, 01.09.2016, p. 406-419.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Active Sensing of Social Networks
AU - Wai, Hoi To
AU - Scaglione, Anna
AU - Leshem, Amir
PY - 2016/9/1
Y1 - 2016/9/1
N2 - 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.
AB - 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.
KW - DeGroot model
KW - opinion dynamics
KW - social networks
KW - sparse recovery
KW - system identification
UR - http://www.scopus.com/inward/record.url?scp=85027709921&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027709921&partnerID=8YFLogxK
U2 - 10.1109/TSIPN.2016.2555785
DO - 10.1109/TSIPN.2016.2555785
M3 - Article
AN - SCOPUS:85027709921
VL - 2
SP - 406
EP - 419
JO - IEEE Transactions on Signal and Information Processing over Networks
JF - IEEE Transactions on Signal and Information Processing over Networks
SN - 2373-776X
IS - 3
M1 - 7456339
ER -