CommPar: A Community-Based Model Partitioning Approach for Large-Scale Networked Social Dynamics Simulation

Efficient large-scale simulation on multiple processors is essential for social dynamics study but still has been proved to be a challenge. Community structure is a ubiquitous property of social networks. It has significant influence on its dynamics and leads the selection of model partition algorithms a critical performance issue. However, the underlying community structure is not well exploited by existing approaches of load-balancing optimizations, which discounted their effectiveness. This paper proposes COMMPAR, a community-based model partitioning approach, which utilizes the community information of social networks for performance tuning. It contains a two-phased network model partitioning as follows: first, community detection algorithm is employed to discover community structure residing in large-scale social networks, second, those communities are further equally partitioned to achieve an appropriate configuration of simulation execution, and facilitates mapping of the communities onto multiple computer processors. Eventually, the experimental results of a random-walk dynamics simulation show that COMMPAR significantly outperforms several existing partitioning approaches, and can efficiently reduce the overhead of interprocessor communications.