University of Limerick
Faqueeh_2018_Influence.pdf (2.6 MB)

Influence maximization in noisy networks

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journal contribution
posted on 2023-01-09, 15:19 authored by Şirag Erkol, Ali Faqeeh, Filippo Radicchi
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 does not 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


Dynamics of the metabolic state in the context of a systematic approach to the study of the processes of growth and development of higher plants and fungi

Russian Foundation for Basic Research

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EPL (Europhysics Letters);123 (5)


IOP Publishing


peer-reviewed The full text of this article will not be available in ULIR until the embargo expires on the 05/10/2019

Other Funding information

SFI, National Science Foundation, US Army Research Office



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