Adversarial Influence Maximization
This addresses a security or robustness challenge in social network analysis, but it appears incremental as it extends existing influence maximization frameworks to adversarial settings.
The paper tackles the problem of influence maximization in networks under adversarial conditions, where an adversary controls the spread of contagion, and establishes theoretical bounds on minimax pseudo-regret for both undirected and directed networks.
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.