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In th is paper, three k inds o f tem poral ce n tra lity o f nodes in com plex ne tw orks were predicted. T h ro u g h the analysis o f the tem poral cen tra lity values o f nodes at d iffe ren t tim es in the real datasets, i t can be found th a t tem poral ce n tra lity values o f nodes in d iffe re n t tim es are h ig h ly correlated. Based on th is observation, we proposed several predic­ t io n methods to p red ic t the tem poral cen tra lity values o f nodes in the fu tu re in real datasets. T he n , th rough the e rro r analysis between the real values and predicted values ? the performance o f d iffe re n t p red ic tion methods in d iffe re n t real data sets was compared. T he resu lts show th a t the recent weighted average m ethod perform s best in the M IT rea lity trace ,and the recent u n ifo rm average m ethod perform s best in the In focom 06 trace.

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).