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Influence Maximization Problem (IMP) is selecting a seed set of nodes in thesocial network to spread the influence as widely as possible. It has manyapplications in multiple domains, e.g., viral marketing is frequently used fornew products or activities advertisements. While it is a classic andwell-studied problem in computer science, unfortunately, all those proposedtechniques are compromising among time efficiency, memory consumption, andresult quality. In this paper, we conduct comprehensive experimental studies onthe state-of-the-art IMP approximate approaches to reveal the underlyingtrade-off strategies. Interestingly, we find that even the state-of-the-artapproaches are impractical when the propagation probability of the network havebeen taken into consideration. With the findings of existing approaches, wepropose a novel residual-based approach (i.e., RCELF) for IMP, which i)overcomes the deficiencies of existing approximate approaches, and ii) providestheoretical guaranteed results with high efficiency in both time- and space-perspectives. We demonstrate the superiority of our proposal by extensiveexperimental evaluation on real datasets.

RCELF: A Residual-based Approach for Influence Maximization Problem