A new algorithm for the influence maximization problem in dynamic networks or traffic sensor networks

Influence spread is one of the key problems in complex networks, and the results of influence maximization problem (IMP) based on dynamic networks are less. In this paper, we discuss the dynamic IMP and describe the dynamic independent cascade model (DICM) and the dynamic linear threshold model (DLTM). We also conclude that IMP based on DICM and DLTM is NP-Hard. To solve the IMP, we present an improved greedy algorithm that is validated based on four datasets with different sizes. Our findings indicate that, compared with the HT algorithm, the size of the influence spread of our algorithm has an obvious advantage, and time efficiency is better than that of the HT algorithm.