Influence Maximization in Dynamic Social Networks Under Partially Observable Environments

Influence and influence diffusion have been studied widely in social networks. Influence maximization is the problem of detecting a set of influential nodes in a social network, which represents relationships among individuals. 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 where the changes that occur over time can be observed by periodically probing nodes to update their connections. The goal of this work is to probe a subset of nodes in a social network so that the actual influence diffusion process in the network can be best uncovered with the probing nodes. We propose three algorithms, MaxC, MaxDC and MaxT, to approximate the optimal solution with probing nodes, which achieve improvement on estimating the number of activated nodes over state-of-the-art-method by 2.88%, 4.95% and 5.39%, respectively.