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由于人们之间社会关系相对稳定并且存在一定的依赖性,由人携带设备组成的机会网络中会出现节点的聚集现象,从而表现出很好的社团特性.提出一种应用贝叶斯-蒙特卡洛(Bayesian-MCMC)预测机会网络节点社团分配的新方法,并在两个不同地点的机会网络数据集上对该方法进行了评估,实验结果显示,此方法能对机会网络中的社团演变进行预测,达到了很高的准确率,且具有良好的鲁棒性.对机会网络社团快速准确的预测有利于机会网络中节点的管理,消息的传输,资源的分配,并可以为探索由人携带设备组成的机会网络这类场景的移动模型的数学分析提供理论依据.
Due to the relatively stable social relations between people and the existence of a certain degree of dependence, node aggregation occurs in the opportunistic networks composed of people-carrying devices, thus showing good community characteristics.An application of Bayes-Montecat Bayesian-MCMC, a new method for predicting the distribution of societies in opportunistic network nodes and evaluating the method on opportunistic network datasets in two different locations. The experimental results show that this method can be applied to the evolution of social networks in opportunistic networks Prediction, high accuracy and good robustness.A quick and accurate prediction of the opportunistic network community is beneficial to the management of nodes, the transmission of messages and the allocation of resources in opportunistic networks, Equipment to provide opportunistic networks such mathematical model of mobile scenarios to provide a theoretical basis.