Influence maximization in noisy networks

TL;DR

This paper investigates how noise in prior network knowledge affects the identification of influential nodes for spreading processes, revealing that sometimes noisy or incomplete information can still lead to effective influence maximization.

Contribution

It demonstrates that influence maximization can be robust to noise and that adding synthetic errors or losing some knowledge can sometimes improve outcomes.

Findings

Noisy prior information does not always reduce influence spread effectiveness.

Adding synthetic errors can compensate for incorrect dynamical parameters.

In some regimes, complete loss of structural knowledge outperforms partial information.

Abstract

We consider the problem of identifying the most influential nodes for a spreading process on a network when prior knowledge about structure and dynamics of the system is incomplete or erroneous. Specifically, we perform a numerical analysis where the set of top spreaders is determined on the basis of prior information that is artificially altered by a certain level of noise. We then measure the optimality of the chosen set by measuring its spreading impact in the true system. Whereas we find that the identification of top spreaders is optimal when prior knowledge is complete and free of mistakes, we also find that the quality of the top spreaders identified using noisy information doesn't necessarily decrease as the noise level increases. For instance, we show that it is generally possible to compensate for erroneous information about dynamical parameters by adding synthetic errors in the structure of the network. Further, we show that, in some dynamical regimes, even completely losing prior knowledge on network structure may be better than relying on certain but incomplete information.