Credit Distribution for Influence Maximization in Online Social Networks with Time Constraint

Considering the time constraint, influence maximization with time constraint (IMTC) is a problem of identifying several maximum influential individuals as seed nodes who will influence others and lead to the largest number of adoption in an expected sense. Associated with probabilities of events and the radio of information gain, we propose an optimized approach to evaluate the activation probability synthetically. As the credit which indicates the strength of influence given to adjacent neighbors is depended on the optimized activation probability (OAP), we also extend the Credit Distribution (CD) model by restricting the scope of credit distribution with the time-delay aspect of influence diffusion in online social networks. Furthermore, the time obstacle caused by repeated attempts is converted to length of the action propagation augmented paths (APAP). The simulations and experiments implemented on real datasets manifest that our approach is more effectively and efficiently in identifying seed nodes and predicting influence diffusion compared with other related approaches.