Continuous states latency aware influence maximization in social networks

Influence maximization is the problem of finding a small set of nodes that maximizes the aggregated influence in social networks. The problem of influence maximization in social networks has been explored in many previous researches. All the existing methods have mainly relied on the constraint of binary state for each node which is either inactive or active. However in reality, changing the state of a node from inactive to active happens gradually and under the continuous effects of all its inspiring neighbors. Accordingly, a node can be inactive, active, or in any state between these two, indicating the gradual process of inspiration. In this paper, we propose a novel influence propagation model which considers continuous states for each node instead of discrete ones. Furthermore, regarding the major role of time in pairwise propagation rates in social networks, we build on a time constrained framework to solve the influence maximization problem. In our work, we first extend the classic IC model to include continuous states and also time delays for transitions between states. Second, we find the most influential nodes in social networks considering continuity and time of the influence process simultaneously. And the last but not the least, it is applicable to well-known real social networks such as Epinions and Wikipedia.