Optimal selection of the most informative nodes for a noisy DeGroot model with stubborn agents
(2025) 2025 European Control Conference, ECC 2025 p.558-563- Abstract
Finding the optimal subset of individuals to observe in order to obtain the best estimate of the average opinion of a society is a crucial problem in a wide range of applications, including policy-making, strategic business decisions, and the analysis of sociological trends. We consider the opinion vector X to be updated according to a DeGroot opinion dynamical model with stubborn agents, subject to perturbations from external random noise, which can be interpreted as transmission errors. The objective function of the optimization problem is the variance reduction achieved by observing the equilibrium opinions of a subset KV of agents. We demonstrate that, under this specific setting, the objective function exhibits the property of... (More)
Finding the optimal subset of individuals to observe in order to obtain the best estimate of the average opinion of a society is a crucial problem in a wide range of applications, including policy-making, strategic business decisions, and the analysis of sociological trends. We consider the opinion vector X to be updated according to a DeGroot opinion dynamical model with stubborn agents, subject to perturbations from external random noise, which can be interpreted as transmission errors. The objective function of the optimization problem is the variance reduction achieved by observing the equilibrium opinions of a subset KV of agents. We demonstrate that, under this specific setting, the objective function exhibits the property of submodularity. This allows us to effectively design a Greedy Algorithm to solve the problem, significantly reducing its computational complexity. Simple examples are provided to validate our results.
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- author
- Raineri, Roberta ; Como, Giacomo LU and Fagnani, Fabio
- organization
- publishing date
- 2025
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2025 European Control Conference, ECC 2025
- edition
- 2025
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2025 European Control Conference, ECC 2025
- conference location
- Thessaloniki, Greece
- conference dates
- 2025-06-24 - 2025-06-27
- external identifiers
-
- scopus:105030950091
- ISBN
- 9783907144121
- DOI
- 10.23919/ECC65951.2025.11187098
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2025 EUCA.
- id
- 07cee4e5-757f-45f4-8e33-64252fff9a23
- date added to LUP
- 2026-04-21 16:35:58
- date last changed
- 2026-04-21 16:36:59
@inproceedings{07cee4e5-757f-45f4-8e33-64252fff9a23,
abstract = {{<p>Finding the optimal subset of individuals to observe in order to obtain the best estimate of the average opinion of a society is a crucial problem in a wide range of applications, including policy-making, strategic business decisions, and the analysis of sociological trends. We consider the opinion vector X to be updated according to a DeGroot opinion dynamical model with stubborn agents, subject to perturbations from external random noise, which can be interpreted as transmission errors. The objective function of the optimization problem is the variance reduction achieved by observing the equilibrium opinions of a subset KV of agents. We demonstrate that, under this specific setting, the objective function exhibits the property of submodularity. This allows us to effectively design a Greedy Algorithm to solve the problem, significantly reducing its computational complexity. Simple examples are provided to validate our results.</p>}},
author = {{Raineri, Roberta and Como, Giacomo and Fagnani, Fabio}},
booktitle = {{2025 European Control Conference, ECC 2025}},
isbn = {{9783907144121}},
language = {{eng}},
pages = {{558--563}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Optimal selection of the most informative nodes for a noisy DeGroot model with stubborn agents}},
url = {{http://dx.doi.org/10.23919/ECC65951.2025.11187098}},
doi = {{10.23919/ECC65951.2025.11187098}},
year = {{2025}},
}