  Influence Maximization Problem (IMP) is selecting a seed set of nodes in the
social network to spread the influence as widely as possible. It has many
applications in multiple domains, e.g., viral marketing is frequently used for
new products or activities advertisements. While it is a classic and
well-studied problem in computer science, unfortunately, all those proposed
techniques are compromising among time efficiency, memory consumption, and
result quality. In this paper, we conduct comprehensive experimental studies on
the state-of-the-art IMP approximate approaches to reveal the underlying
trade-off strategies. Interestingly, we find that even the state-of-the-art
approaches are impractical when the propagation probability of the network have
been taken into consideration. With the findings of existing approaches, we
propose a novel residual-based approach (i.e., RCELF) for IMP, which i)
overcomes the deficiencies of existing approximate approaches, and ii) provides
theoretical guaranteed results with high efficiency in both time- and space-
perspectives. We demonstrate the superiority of our proposal by extensive
experimental evaluation on real datasets.
