Controlling a Social Network of Individuals with Coevolving Actions and Opinions

Fuente: arXiv
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Main Authors: Raineri, Roberta, Ye, Mengbin, Zino, Lorenzo
Format: Preprint
Published: 2025
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author Raineri, Roberta
Ye, Mengbin
Zino, Lorenzo
author_facet Raineri, Roberta
Ye, Mengbin
Zino, Lorenzo
contents In this paper, we consider a population of individuals who have actions and opinions, which coevolve, mutually influencing one another on a complex network structure. In particular, we formulate a control problem for this social network, in which we assume that we can inject into the network a committed minority -- a set of stubborn nodes -- with the objective of steering the population, initially at a consensus, to a different consensus state. Our study focuses on two main objectives: i) determining the conditions under which the committed minority succeeds in its goal, and ii) identifying the optimal placement for such a committed minority. After deriving general monotone convergence result for the controlled dynamics, we leverage these results to build a computationally-efficient algorithm to solve the first problem and an effective heuristics for the second problem, which we prove to be NP-complete. For both algorithms, we establish theoretical guarantees. The proposed methodology is illustrated though academic examples, and demonstrated on a real-world case study.
format Preprint
id arxiv_https___arxiv_org_abs_2504_06913
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Controlling a Social Network of Individuals with Coevolving Actions and Opinions
Raineri, Roberta
Ye, Mengbin
Zino, Lorenzo
Systems and Control
Dynamical Systems
In this paper, we consider a population of individuals who have actions and opinions, which coevolve, mutually influencing one another on a complex network structure. In particular, we formulate a control problem for this social network, in which we assume that we can inject into the network a committed minority -- a set of stubborn nodes -- with the objective of steering the population, initially at a consensus, to a different consensus state. Our study focuses on two main objectives: i) determining the conditions under which the committed minority succeeds in its goal, and ii) identifying the optimal placement for such a committed minority. After deriving general monotone convergence result for the controlled dynamics, we leverage these results to build a computationally-efficient algorithm to solve the first problem and an effective heuristics for the second problem, which we prove to be NP-complete. For both algorithms, we establish theoretical guarantees. The proposed methodology is illustrated though academic examples, and demonstrated on a real-world case study.
title Controlling a Social Network of Individuals with Coevolving Actions and Opinions
topic Systems and Control
Dynamical Systems
url https://arxiv.org/abs/2504.06913