| 复杂网络中节点暂态中心性预测研究 |
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| 引用本文： | 童林萍,徐守志,周欢,蒋廷耀.复杂网络中节点暂态中心性预测研究[J].计算机科学,2017,44(10):122-126. |
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| 作者姓名： | 童林萍 徐守志 周欢 蒋廷耀 |
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| 作者单位： | 三峡大学计算机与信息学院 宜昌443002,三峡大学计算机与信息学院 宜昌443002,三峡大学计算机与信息学院 宜昌443002,三峡大学计算机与信息学院 宜昌443002 |
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| 基金项目： | 本文受国家重点研发计划(2016YFB0800403),国家自然科学基金(61174177,2,41172298),湖北省自然科学基金(2017CFB594)资助 |
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| 摘 要： | 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。
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| 关 键 词： | 复杂网络 暂态中心性 真实数据集 预测方法 |
| 收稿时间： | 2016/8/17 0:00:00 |
| 修稿时间： | 2016/12/12 0:00:00 |
Research on Temporal Centrality Prediction of Nodes in Complex Networks |
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| TONG Lin-ping,XU Shou-zhi,ZHOU Huan and JIANG Ting-yao.Research on Temporal Centrality Prediction of Nodes in Complex Networks[J].Computer Science,2017,44(10):122-126. |
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| Authors: | TONG Lin-ping XU Shou-zhi ZHOU Huan and JIANG Ting-yao |
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| Affiliation: | College of Computer and Information Technology,China Three Gorges University,Yichang 443002,China,College of Computer and Information Technology,China Three Gorges University,Yichang 443002,China,College of Computer and Information Technology,China Three Gorges University,Yichang 443002,China and College of Computer and Information Technology,China Three Gorges University,Yichang 443002,China |
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| Abstract: | 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. |
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| Keywords: | Complex networks Temporal centrality Real datasets Prediction methods |
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