Adversarial Influence Maximization

TL;DR

This paper studies influence maximization in networks under adversarial conditions, formulating it as a repeated game to determine optimal seeding strategies and bounds on regret.

Contribution

It introduces a game-theoretic framework for influence maximization in adversarial settings and derives bounds on minimax pseudo-regret for various network types.

Findings

Established upper and lower bounds on minimax pseudo-regret.

Formulated influence maximization as a repeated game.

Analyzed both directed and undirected networks.

Abstract

We consider the problem of influence maximization in fixed networks for contagion models in an adversarial setting. The goal is to select an optimal set of nodes to seed the influence process, such that the number of influenced nodes at the conclusion of the campaign is as large as possible. We formulate the problem as a repeated game between a player and adversary, where the adversary specifies the edges along which the contagion may spread, and the player chooses sets of nodes to influence in an online fashion. We establish upper and lower bounds on the minimax pseudo-regret in both undirected and directed networks.