Opinion Shaping in Social Networks Using Reinforcement Learning - Pôle Cyber & Réseaux Access content directly
Preprints, Working Papers, ... Year : 2022

Opinion Shaping in Social Networks Using Reinforcement Learning

Abstract

In this article, we consider a variant of the classical DeGroot model of opinion propagation with random interactions, in which a prescribed subset of agents is amenable to a control parameter. There are also some stubborn agents and some agents that are neither stubborn nor amenable to control. We map the problem to a shortest path problem, where the control parameter is coupled across controlled nodes because of a common resource constraint. Hence, the problem is not amenable to a pure dynamic programming approach, and the classical reinforcement learning schemes for the latter cannot be applied here for maximizing average influence in the long run. We view it instead as a parametric optimization problem and not a control problem and use a nonclassical policy gradient scheme. We analyze its performance theoretically and through numerical experiments. We also consider a situation when only certain interactions between agents are observed.
Fichier principal
Vignette du fichier
1910.08802.pdf (2 Mo) Télécharger le fichier
1910.08802 (1).pdf (2 Mo) Télécharger le fichier
Opinion_Shaping_in_Social_Networks_Using_Reinforcement_Learning.pdf (2.37 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Origin : Files produced by the author(s)

Dates and versions

hal-04327548 , version 1 (11-12-2023)

Identifiers

Cite

Vivek S Borkar, Alexandre Reiffers-Masson. Opinion Shaping in Social Networks Using Reinforcement Learning. 2023. ⟨hal-04327548⟩
7 View
7 Download

Altmetric

Share

Gmail Facebook X LinkedIn More