Correlation Robust Influence Maximization

Louis Chen, Divya Padmanabhan, Chee Chin Lim, Karthik Natarajan

Abstract:

We propose a distributionally robust model for the influence maximization problem.
Unlike the classic independent cascade model [16], 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.