Adversarial Influence Maximization
Social and Information Networks
2019-01-23 v2 Machine Learning
Machine Learning
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.
Cite
@article{arxiv.1611.00350,
title = {Adversarial Influence Maximization},
author = {Justin Khim and Varun Jog and Po-Ling Loh},
journal= {arXiv preprint arXiv:1611.00350},
year = {2019}
}
Comments
30 pages