Correlation Robust Influence Maximization

TL;DR

This paper introduces a distributionally robust influence maximization model that accounts for worst-case correlations in influence spread, providing efficient computation and approximation guarantees, and highlighting limitations of traditional independent models.

Contribution

It develops a novel adversarially adapted influence maximization framework with provable approximation guarantees and analyzes its structural properties and practical implications.

Findings

Efficient computation of worst-case influence under correlation uncertainty

Approximation guarantee of (1 - 1/e) for the NP-hard optimization

Numerical experiments comparing robust and traditional influence models

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.