Concurrent conversation modeling and parallel simulation of the naming game in social networks

K. Perumalla
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引用次数: 2

Abstract

The Naming Game is an effective self-organization model to understand the emergence of linguistic consensus and to investigate the system dynamics in a variety of phenomena over social networks of autonomous agents. The Naming Game is an effective description for the evolution of consensus despite the absence of any central coordination or specialized initialization even in large-scale networks. While the classical game is effective in description, it was defined with inherently sequential evaluation semantics over the entire network. Here, we develop a new concurrent model as a relaxation of the classical formulation and express it in a discrete event style of evaluation. Further, with the uncovered concurrency that was absent in the classical algorithm, we map the concurrent model to parallel discrete event simulation. Using a prototype implementation, we present an initial parallel performance study on networks containing hundreds of thousands of individuals, with a decrease in simulation time in the best-observed case from 4800 seconds down to 1400 seconds.
社交网络中命名游戏的并发会话建模与并行仿真
命名游戏是一种有效的自组织模型,可以用来理解语言共识的产生,并研究自治主体社会网络中各种现象的系统动力学。命名游戏是一种有效的描述共识演变的方法,即使在大规模网络中也没有任何中心协调或专门的初始化。虽然经典博弈在描述上是有效的,但它是在整个网络上用固有的顺序评估语义定义的。在这里,我们开发了一个新的并发模型,作为经典公式的松弛,并以离散事件的评估方式表示它。此外,利用经典算法中未发现的并发性,将并发模型映射到并行离散事件仿真中。使用原型实现,我们在包含数十万个个体的网络上进行了初始并行性能研究,在最佳观察情况下,模拟时间从4800秒减少到1400秒。
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