复杂网络中节点暂态中心性预测研究

Research on Temporal Centrality Prediction of Nodes in Complex Networks

对复杂网络中节点的3种暂态中心性进行了预测研究。通过在真实数据集中分析节点不同时刻的暂态中心性值发现,不同时刻节点的暂态中心性具有很强的相关性。基于此,提出几种预测方法对真实数据集中节点未来的暂态中心性值进行预测。通过对真实值与预测值进行误差分析,比较了不同预测方法在不同真实数据中的预测性能。结果表明,在MIT数据集中,最近时窗加权平均方法的性能最好;在Infocom 06数据集中,最近时窗平均方法的性能最好。

In this paper,three kinds of temporal centrality of nodes in complex networks were predicted.Through the analysis of the temporal centrality values of nodes at different times in the real datasets,it can be found that temporal centrality values of nodes in different times are highly correlated.Based on this observation,we proposed several prediction methods to predict the temporal centrality values of nodes in the future in real datasets.Then,through the error analysis between the real values and predicted values,the performance of different prediction methods in different real data sets was compared.The results show that the recent weighted average method performs best in the MIT reality trace,and the recent uniform average method performs best in the Infocom 06 trace.