Influence and influence diffusion have been studied widely in social networks. Although, most of the existing works on this task focus on static networks, in this paper we study the problem of influence maximization in dynamic social networks. More specifically, the network changes over time and the changes can be observed by periodically probing communities of nodes to update their connections. The goal of this work is to probe part of communities in a social network so that the actual influence diffusion process in the network can be best uncovered with the probing communities. We propose an algorithm to approximate the optimal solution with probing communities achieving improvement on estimating the number of infected nodes state-of-the-art-method by 21%.