Correlation Robust Influence Maximization

arXiv2010.14620MaRDI QIDQ6352396

Author name not available (Why is that?)

Publication date: 24 October 2020

Abstract: We propose a distributionally robust model for the influence maximization problem. Unlike the classic independent cascade model citep{kempe2003maximizing}, this model's diffusion process is adversarially adapted to the choice of seed set. Hence, instead of optimizing under the assumption that all influence relationships in the network are independent, we seek a seed set whose expected influence under the worst correlation, i.e. the "worst-case, expected influence", is maximized. We show that this worst-case influence can be efficiently computed, and though the optimization is NP-hard, a ($1-1/e$) approximation guarantee holds. We also analyze the structure to the adversary's choice of diffusion process, and contrast with established models. Beyond the key computational advantages, we also highlight the extent to which the independence assumption may cost optimality, and provide insights from numerical experiments comparing the adversarial and independent cascade model.

Has companion code repository: https://github.com/justanothergithubber/corr-im

This page was built for publication: Correlation Robust Influence Maximization

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6352396)