计算机科学 ›› 2017, Vol. 44 ›› Issue (10): 122-126.doi: 10.11896/j.issn.1002-137X.2017.10.024

• 网络与通信 • 上一篇    下一篇

复杂网络中节点暂态中心性预测研究

童林萍,徐守志,周欢,蒋廷耀   

  1. 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
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    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

Research on Temporal Centrality Prediction of Nodes in Complex Networks

TONG Lin-ping, XU Shou-zhi, ZHOU Huan and JIANG Ting-yao   

  • Online:2018-12-01 Published:2018-12-01

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。

关键词: 复杂网络,暂态中心性,真实数据集,预测方法

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.

Key words: Complex networks,Temporal centrality,Real datasets,Prediction methods

[1] 郭雷,许晓鸣.复杂网络[M].上海:上海科技教育出版社,2006:1-283.
[2] 汪小帆,李翔,陈关荣.复杂网络理论及其应用[M].北京:清华大学出版社,2006.
[3] BOCCALETTI S,LATORA V,MORENO Y,et al.ComplexNetworks:Structure and Dynamics[J].Physics Reports,2015,424(4/5):175-308.
[4] ZHOU H,WU J,ZHAO H,et al.Incentive-Driven and Freshness-Aware Content Dissemination in Selfish Opportunistic Mobile Networks[J].IEEE Transactions on Parallel and Distributed System,2015,26(9):2493-2505.
[5] LAI Y C,MOTTER A E,NISHIKAWA T.Attacks and Cascades in Complex Networks[M]∥Complex Networks.2004:299-310.
[6] 汪小帆.网络科学导论[M].北京:高等教育出版社,2012.
[7] FREEMAN L C.Centrality in social networks conceptual clarification[J].Social Networks,1978,1(3):215-239.
[8] GAO W,LI Q,ZHAO B,et al.Multicasting in delay tolerant networks:a social network perspective[C]∥ Tenth ACM International Symposium on Mobile Ad Hoc NETWORKING and Computing.ACM,2009:299-308.
[9] FAN J,CHEN J,DU Y,et al.Geo-community-based broadcas-ting for data dissemination in mobile social networks[J].IEEE Transactions on Parallel and Distributed Systems,2013,24(4):734-743.
[10] LIU X,LI P,LIU J,et al.Centrality for nodes in social networks[J].Computer Engineering and Applications,2014,0(5):116-120.(in Chinese) 刘欣,李鹏,刘璟,等.社交网络节点中心性测度[J].计算机工程与应用,2014(5):116-120.
[11] SONG Y P,NI J.Effect of variable network clustering on the accuracy of node centrality[J].Acta Physica Sinica,2016,65(2):28901.(in Chinese) 宋玉萍,倪静.网络集聚性对节点中心性指标的准确性影响研究[J].物理学报,2016,65(2):28901.
[12] WU D P,JIN J W,LV Y,et al.Similarity Aware Community Detecting Method for Social Intermittently Connected Mobile Network[J].Journal of Electronics & Information Technology.2013,5(1):141-146.(in Chinese) 吴大鹏,靳继伟,吕翊,等.节点相似度感知的社会化间断连接无线网络结构检测机制[J].电子与信息学报,2013,35(1):141-146.
[13] YANG J X,WANG C K,WANG M,et al.Algorithms for Local Betweenness Centrality of Fully Dynamic Multi-Dimensional Networks[J].Journal of Computers,2015(9):1852-1864.(in Chinese) 杨建祥,王朝坤,王萌,等.全动态多维网络局部介数中心度算法[J].计算机学报,2015(9):1852-1864.
[14] KIM,HYOUNGSHICK,ANDERSON R.Temporal node cen-trality in complex networks[J].Physical Review,2012,E85(2):605-624.
[15] KIM H,TANG J,ANDERSON R,et al.Centrality Prediction in Dynamic Human Contact Networks[J].Computer Networks,2012,56(3):983-996.
[16] QIN L,YANG Z L,HUANG S G.Synthesis Evaluation Method for Node Importance in Complex Networks[J].Chinese Journal of Computer Science,2015,2(2):60-64.(in Chinese) 秦李,杨子龙,黄曙光.复杂网络的节点重要性综合评价[J].计算机科学,2015,42(2):60-64.
[17] EAGLE N,PENTLAND A.Reality mining:sensing complex social systems[J].Journal of Personal and Ubiquitous Computing,2006,10(4):255-268.
[18] SCOTT J,HUI P,CROWCROFT J,et al.Haggle:a Networking Architecture Designed Around Mobile Users[C]∥Proceedings of the Third Annual Conference on Wireless On-Demand Network Systems & Services.2006.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 雷丽晖,王静. 可能性测度下的LTL模型检测并行化研究[J]. 计算机科学, 2018, 45(4): 71 -75, 88 .
[2] 夏庆勋,庄毅. 一种基于局部性原理的远程验证机制[J]. 计算机科学, 2018, 45(4): 148 -151, 162 .
[3] 厉柏伸,李领治,孙涌,朱艳琴. 基于伪梯度提升决策树的内网防御算法[J]. 计算机科学, 2018, 45(4): 157 -162 .
[4] 王欢,张云峰,张艳. 一种基于CFDs规则的修复序列快速判定方法[J]. 计算机科学, 2018, 45(3): 311 -316 .
[5] 孙启,金燕,何琨,徐凌轩. 用于求解混合车辆路径问题的混合进化算法[J]. 计算机科学, 2018, 45(4): 76 -82 .
[6] 张佳男,肖鸣宇. 带权混合支配问题的近似算法研究[J]. 计算机科学, 2018, 45(4): 83 -88 .
[7] 伍建辉,黄中祥,李武,吴健辉,彭鑫,张生. 城市道路建设时序决策的鲁棒优化[J]. 计算机科学, 2018, 45(4): 89 -93 .
[8] 刘琴. 计算机取证过程中基于约束的数据质量问题研究[J]. 计算机科学, 2018, 45(4): 169 -172 .
[9] 钟菲,杨斌. 基于主成分分析网络的车牌检测方法[J]. 计算机科学, 2018, 45(3): 268 -273 .
[10] 史雯隽,武继刚,罗裕春. 针对移动云计算任务迁移的快速高效调度算法[J]. 计算机科学, 2018, 45(4): 94 -99, 116 .