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Targeting influential nodes in complex networks allows fastening or hinderingrumors, epidemics, and electric blackouts. Since communities are prevalent inreal-world networks, community-aware centrality measures exploit thisinformation to target influential nodes. Researches show that they comparefavorably with classical measures that are agnostic about the communitystructure. Although the diffusion process is of prime importance, previousstudies consider mainly the famous Susceptible-Infected-Recovered (SIR)epidemic propagation model. This work investigates the consistency of previousanalyses using the popular Linear Threshold (LT) propagation model, whichcharacterizes many spreading processes in our real life. We perform acomparative analysis of seven influential community-aware centrality measureson thirteen real-world networks. Overall, results show that Community-basedMediator, Comm Centrality, and Modularity Vitality outperform the othermeasures. Moreover, Community-based Mediator is more effective on a tightbudget (i.e., a small fraction of initially activated nodes), while CommCentrality and Modularity Vitality perform better with a medium to a highfraction of initially activated nodes.

Analyzing Community-aware Centrality Measures Using The Linear Threshold Model