Optimal interventions in opinion dynamics on large-scale, time-varying, random networks
(2025) 64th IEEE Conference on Decision and Control, CDC 2025 In Proceedings of the IEEE Conference on Decision and Control p.8083-8090- Abstract
We consider two optimization problems in which a planner aims to influence the average transient opinion in the Friedkin-Johnsen dynamics on a network by intervening on the agents' innate opinions. Solving these problems requires full network knowledge, which is often not available because of the cost involved in collecting this information or due to privacy considerations. For this reason, we focus on intervention strategies that are based on statistical instead of exact knowledge of the network. We focus on a time-varying random network model where the network is resampled at each time step and formulate two intervention problems in this setting. We show that these problems can be casted into mixed integer linear programs in the type... (More)
We consider two optimization problems in which a planner aims to influence the average transient opinion in the Friedkin-Johnsen dynamics on a network by intervening on the agents' innate opinions. Solving these problems requires full network knowledge, which is often not available because of the cost involved in collecting this information or due to privacy considerations. For this reason, we focus on intervention strategies that are based on statistical instead of exact knowledge of the network. We focus on a time-varying random network model where the network is resampled at each time step and formulate two intervention problems in this setting. We show that these problems can be casted into mixed integer linear programs in the type space, where the type of a node captures its out- and in-degree and other local features of the nodes, and provide a closed form solution for one of the two problems. The integer constraints may be easily removed using probabilistic interventions leading to linear programs. Finally, we show by a numerical analysis that there are cases in which the derived optimal interventions on time-varying networks can lead to close to optimal interventions on fixed networks.
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- author
- Cianfanelli, Leonardo ; Como, Giacomo LU ; Fagnani, Fabio ; Ozdaglar, Asuman and Parise, Francesca
- organization
- publishing date
- 2025
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Friedkin-Johnsen dynamics, Intervention design, Random networks, Time-Varying networks
- host publication
- 2025 IEEE 64th Conference on Decision and Control, CDC 2025
- series title
- Proceedings of the IEEE Conference on Decision and Control
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 64th IEEE Conference on Decision and Control, CDC 2025
- conference location
- Rio de Janeiro, Brazil
- conference dates
- 2025-12-09 - 2025-12-12
- external identifiers
-
- scopus:105031894217
- ISSN
- 2576-2370
- 0743-1546
- ISBN
- 9798331526276
- DOI
- 10.1109/CDC57313.2025.11312041
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2025 IEEE.
- id
- c3581cbf-c602-457b-8fdd-91556b946b36
- date added to LUP
- 2026-05-18 13:12:08
- date last changed
- 2026-05-18 13:13:15
@inproceedings{c3581cbf-c602-457b-8fdd-91556b946b36,
abstract = {{<p>We consider two optimization problems in which a planner aims to influence the average transient opinion in the Friedkin-Johnsen dynamics on a network by intervening on the agents' innate opinions. Solving these problems requires full network knowledge, which is often not available because of the cost involved in collecting this information or due to privacy considerations. For this reason, we focus on intervention strategies that are based on statistical instead of exact knowledge of the network. We focus on a time-varying random network model where the network is resampled at each time step and formulate two intervention problems in this setting. We show that these problems can be casted into mixed integer linear programs in the type space, where the type of a node captures its out- and in-degree and other local features of the nodes, and provide a closed form solution for one of the two problems. The integer constraints may be easily removed using probabilistic interventions leading to linear programs. Finally, we show by a numerical analysis that there are cases in which the derived optimal interventions on time-varying networks can lead to close to optimal interventions on fixed networks.</p>}},
author = {{Cianfanelli, Leonardo and Como, Giacomo and Fagnani, Fabio and Ozdaglar, Asuman and Parise, Francesca}},
booktitle = {{2025 IEEE 64th Conference on Decision and Control, CDC 2025}},
isbn = {{9798331526276}},
issn = {{2576-2370}},
keywords = {{Friedkin-Johnsen dynamics; Intervention design; Random networks; Time-Varying networks}},
language = {{eng}},
pages = {{8083--8090}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{Proceedings of the IEEE Conference on Decision and Control}},
title = {{Optimal interventions in opinion dynamics on large-scale, time-varying, random networks}},
url = {{http://dx.doi.org/10.1109/CDC57313.2025.11312041}},
doi = {{10.1109/CDC57313.2025.11312041}},
year = {{2025}},
}