The modeling and simulation of social networks is an important approach to better understanding complex social phenomena, especially when the inner structure has remarkable impact on behavior. With the availability of unprecedented data sets, simulating large-scale social networks of millions, or even billions, of entities has become a new challenge. Current simulation environments for social studies are mostly sequential and may not be efficient when social networks grow to a certain size. In order to facilitate large-scale social network modeling and simulation, this paper proposes a framework named SUPE-Net, which is based on a parallel discrete event simulation environment YH-SUPE for massively parallel architectures. The framework is designed as a layered architecture with utilities for network generation, algorithms and agent-based modeling. Distributed adjacency lists are used for graph modeling and a reaction–diffusion paradigm is adapted to model dynamical processes. Experiments are performed using PageRank and the susceptible–infected–recovered (SIR) model on social networks with millions of entities. The results demonstrate that SUPE-Net has achieved a speedup of 12, and increased the event-processing rate by 11%, with good scalability and effectiveness.