  Cost-aware Targeted Viral Marketing (CTVM), a generalization of Influence
Maximization (IM), has received a lot of attentions recently due to its
commercial values. Previous approximation algorithms for this problem required
a large number of samples to ensure approximate guarantee. In this paper, we
propose an efficient approximation algorithm which uses fewer samples but
provides the same theoretical guarantees based on generating and using
important samples in its operation. Experiments on real social networks show
that our proposed method outperforms the state-of-the-art algorithm which
provides the same approximation ratio in terms of the number of required
samples and running time.
