Related papers: Correlation Robust Influence Maximization

Correlation Robust Influence Maximization

URL: http://arxiv.org/abs/2010.14620v2
Date: Tue, 22 Feb 2022 05:51:28 GMT
Title: Correlation Robust Influence Maximization
Authors: Louis Chen, Divya Padmanabhan, Chee Chin Lim, Karthik Natarajan
Abstract summary: We propose a distributionally robust model for the influence problem. We seek a seed set whose expected influence under the worst correlation is maximized. We show that this worst-case influence can be efficiently computed.
Score: 5.508091917582913
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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