  With the popularity of OSNs, finding a set of most influential users (or
nodes) so as to trigger the largest influence cascade is of significance. For
example, companies may take advantage of the "word-of-mouth" effect to trigger
a large cascade of purchases by offering free samples/discounts to those most
influential users. This task is usually modeled as an influence maximization
problem, and it has been widely studied in the past decade. However,
considering that users in OSNs may participate in various kinds of online
activities, e.g., giving ratings to products, joining discussion groups, etc.,
influence diffusion through online activities becomes even more significant.
  In this paper, we study the impact of online activities by formulating the
influence maximization problem for social-activity networks (SANs) containing
both users and online activities. To address the computation challenge, we
define an influence centrality via random walks to measure influence, then use
the Monte Carlo framework to efficiently estimate the centrality in SANs.
Furthermore, we develop a greedy-based algorithm with two novel optimization
techniques to find the most influential users. By conducting extensive
experiments with real-world datasets, we show our approach is more efficient
than the state-of-the-art algorithm IMM[17] when we needs to handle large
amount of online activities.
