An Investment Framework for Continuous Time Opinion Dynamics on Networks

• 成果类型: Article
• 署名作者: Dhamal, Swapnil; Ben-Ameur, Walid; Chahed, Tijani
• 署名单位: Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Ropar; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Telecom SudParis
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3561099
• 发表日期: 2025
• 页码: 6497-6512
• 关键词: investment; vectors; Social networking (online); Heuristic algorithms; Adaptation models; linear programming; Approximation algorithms; training; Finite element analysis; Data mining; continuous time; Iterative learning control; opinion control; social networks

摘要

The literature on investment strategies for steering opinion dynamics on networks primarily focuses on discrete time models. This work proposes an investment framework for a continuous time model that captures essential factors, such as opinion bias, network effect, and camp investment, by extending Friedkin-Johnsen and Taylor's models. We, hence, devise an optimal investment strategy in the presence of various constraints and present its properties, such as memorylessness. We also show that there is a certain adaptability benefit even in the asymptotic regime. When the network structure and the values of the model parameters are not known, we study the learning aspect in two scenarios: first, when opinions of all the nodes are observable, and second, when opinions of a sample set of nodes are observable. We conclude with a simulation study on representative real-world networks and present insights. When all the nodes' opinions are observable, we show that our learning algorithm deduces the exact optimal strategy in very few intervals. When opinions of a sample set of nodes are observable, our learning algorithm is highly accurate and robust for a large enough sample size.