Optimal investment strategies for competing camps in a social network: a broad framework - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Network Science and Engineering Année : 2019

Optimal investment strategies for competing camps in a social network: a broad framework

Résumé

We study the problem of optimally investing in nodes of a social network in a competitive setting, wherein two camps aim to drive the average opinion of the population in their own favor. Using a well-established model of opinion dynamics, we formulate the problem as a zero-sum game with its players being the two camps. We derive optimal investment strategies for both camps, and show that a random investment strategy is optimal when the underlying network follows a popular class of weight distributions. We study a broad framework, where we consider various well-motivated settings of the problem, namely, when the influence of a camp on a node is a concave function of its investment on that node, when a camp aims at maximizing competitor's investment or deviation from its desired investment, and when one of the camps has uncertain information about the values of the model parameters. We also study a Stackelberg variant of this game under common coupled constraints on the combined investments by the camps and derive their equilibrium strategies, and hence quantify the first-mover advantage. For a quantitative and illustrative study, we conduct simulations on real-world datasets and provide results and insights.
Fichier principal
Vignette du fichier
ODSN_HAL.pdf (3.85 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01712288 , version 1 (19-02-2018)
hal-01712288 , version 2 (24-06-2018)
hal-01712288 , version 3 (20-11-2018)

Identifiants

Citer

Swapnil Dhamal, Walid Ben-Ameur, Tijani Chahed, Eitan Altman. Optimal investment strategies for competing camps in a social network: a broad framework. IEEE Transactions on Network Science and Engineering, 2019, 6 (4), pp.628-645. ⟨10.1109/TNSE.2018.2864575⟩. ⟨hal-01712288v3⟩
486 Consultations
219 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More