On a Network Centrality Maximization Game

We study a network formation game where n players, identified with the nodes of a directed graph to be formed, choose where to wire their outgoing links in order to maximize their PageRank centrality. Specifically, the action of every player i consists in the wiring of a predetermined number d i of directed out-links, and her utility is her own PageRank centrality in the network resulting from the actions of all players. We show that this is a potential game and that the best response correspondence always exhibits a local structure in that it is never convenient for a node i to link to other nodes that are at incoming distance more than d i from her. We then study the equilibria of this game determining necessary conditions for a graph to be a (strict, recurrent) Nash equilibrium. Moreover, in the homogeneous case, where players all have the same number d of out-links, we characterize the structure of the potential-maximizing equilibria, and in the special cases d = 1 and d = 2, we provide a complete classification of the set of (strict, recurrent) Nash equilibria. Our analysis shows in particular that the considered formation mechanism leads to the emergence of undirected and disconnected or loosely connected networks. Funding: This research was carried out within the framework of the Ministero dell’Università e della Ricerca (MIUR)-funded Progetto di Eccellenza of the Dipartimento di Scienze Matematiche G. L. Lagrange, Politecnico di Torino [CUP: E11G18000350001]. It received partial support from the MIUR-funded project PRIN 2017 “Advanced Network Control of Future Smart Grids” and from the Compagnia di San Paolo.

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Publication Details

Journal
Mathematics of Operations Research
Published
2024-08-16
DOI
https://doi.org/10.1287/moor.2022.0251
Primary Topic
Opinion Dynamics and Social Influence
Type
article
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article

On a Network Centrality Maximization Game

Maria Castaldo, Costanza Catalano, Giacomo Como, Fabio Fagnani
Mathematics of Operations Research
Opinion Dynamics and Social Influence
article

On a Network Centrality Maximization Game

Maria Castaldo, Costanza Catalano, Giacomo Como, Fabio Fagnani
article en

Abstract

We study a network formation game where n players, identified with the nodes of a directed graph to be formed, choose where to wire their outgoing links in order to maximize their PageRank centrality. Specifically, the action of every player i consists in the wiring of a predetermined number d i of directed out-links, and her utility is her own PageRank centrality in the network resulting from the actions of all players. We show that this is a potential game and that the best response correspondence always exhibits a local structure in that it is never convenient for a node i to link to other nodes that are at incoming distance more than d i from her. We then study the equilibria of this game determining necessary conditions for a graph to be a (strict, recurrent) Nash equilibrium. Moreover, in the homogeneous case, where players all have the same number d of out-links, we characterize the structure of the potential-maximizing equilibria, and in the special cases d = 1 and d = 2, we provide a complete classification of the set of (strict, recurrent) Nash equilibria. Our analysis shows in particular that the considered formation mechanism leads to the emergence of undirected and disconnected or loosely connected networks. Funding: This research was carried out within the framework of the Ministero dell’Università e della Ricerca (MIUR)-funded Progetto di Eccellenza of the Dipartimento di Scienze Matematiche G. L. Lagrange, Politecnico di Torino [CUP: E11G18000350001]. It received partial support from the MIUR-funded project PRIN 2017 “Advanced Network Control of Future Smart Grids” and from the Compagnia di San Paolo.

Mathematics of Operations ResearchVol. 50(3)
Bank of Italy (IT), Institut polytechnique de Grenoble (FR), Institut national de recherche en sciences et technologies du numérique (FR), Politecnico di Torino (IT), Lund University (SE), Department of Mathematical Sciences (RU), GIPSA-Lab (FR), Turin Polytechnic University (UZ), Université Grenoble Alpes (FR)
Industry, innovation and infrastructure
Openalex Percentile: Top 87%
Opinion Dynamics and Social Influence
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