SUPE-Net: An Efficient Parallel Simulation Environment for Large-Scale Networked Social Dynamics

Many social systems can be described in terms of complex networks of interacting dynamic entities. The research on social system´s dynamics, such as epidemic or rumor spreading, are often suffered from constraints of relatively small to medium scale networks, or dependence on the underlying network assumptions, resulting in limited capacity for dynamics prediction. This paper proposes SUPE-Net, an efficient parallel simulation environment for large-scale social networks. Built on parallel discrete event simulation engine, the SUPE-Net enables high fidelity modeling of social networks with different topologies and complex dynamics protocols. SUPE-Net automatically maps large-scale social networks onto multi-processors and adopts the discrete event fashion to study the complex network dynamics. Different network topologies, including random, small-world, scale-free, and real networks, can be easily constructed and plugged in for comparative study. The execution efficiency of SUPE-Net is demonstrated by simulation experiments of gossip dynamics on the actor networks with millions of entities.