Abstract: The research of influence maximization in social networks is emerging as a promising opportunity for successful viral marketing. Influence maximization with time-delay constraint (IMTC) is to identify a set of initial individuals who will influence others and lead to a maximum value of influence spread consequence under time-delay constraint. Most of the existing models focus on optimizing the simulation consequence of influence spread, and time-delay factors and time-delay constraint are always ignored. The credit distribution with time-delay constraint model (CDTC) incorporates the meeting and activation probabilities to optimize the distribution of credit considering time-delay constraint, and utilizes the optimized relationships of meeting and activation probabilities to evaluate the ability to influence on adjacent individuals. Furthermore, the obstructive effect due to repeated attempts of meeting and activation is reflected by the length of increased propagation paths. After assigning the credit along with the increased propagation paths learned from users action-logs, the nodes which obtain maximal marginal gain are selected to form the seed set by the greedy algorithm with time-delay constraint (GA-TC). The experimental results based on real datasets show that the proposed approach is more accurate and efficient compared with other related methods.