一种基于邻居节点和边的复杂网络节点排序方法——NL中心性算法

doi:10.3976/j.issn.1002-4026.2019.02.017

山东科学 ›› 2019, Vol. 32 ›› Issue (2): 130-136.doi: 10.3976/j.issn.1002-4026.2019.02.017

一种基于邻居节点和边的复杂网络节点排序方法——NL中心性算法

刘书磊¹，杜家乐²，邵增珍^1,3*

1.山东师范大学，山东济南 250014；2.济南市历城一中，山东济南 250115；3.山东女子学院，山东济南 250002

收稿日期:2018-03-03 出版日期:2019-04-20 发布日期:2019-04-02
通信作者: 邵增珍（1976—），男，博士(后)，硕士生导师，研究方向为智能测评系统、网络优化。
作者简介:刘书磊（1993—），男，硕士研究生，研究方向为复杂网络。
基金资助:
中国博士后科学基金（2016M592697）；山东省科技计划发展项目（2014GGH201022）

An ranking method for nodes in complex networks based on neighbor nodes and the lines——NL centrality algorithm

LIU Shu-lei¹，DU Jia-le²，SHAO Zeng-zhen^1,3*

1.Shandong Normal University，Jinan 250014, China；2.Jinan Licheng NO.1 Middle School，Jinan 250115,China； 3.Shandong Women's University, Jinan 250002,China

Received:2018-03-03 Online:2019-04-20 Published:2019-04-02

摘要/Abstract

摘要： 提出了一种基于邻居节点和边的多属性排序方法——NL中心性算法，该算法不仅使用邻居节点和次邻居节点的个数进一步区分了节点的位置，而且还考虑了边对节点重要性的影响。实验结果表明，NL中心性算法在准确性及运行效率方面都优于其他中心性算法。

关键词: 复杂网络, 节点影响力, 邻居节点, 边的重要性

Abstract: A multi-attribute ranking method based on neighbor nodes and lines，NL centrality algorithm，was proposed in this paper. NL centrality algorithm not only utilized the number of neighbors and sub-neighbors to further distinguish the node position, but also fully considered the effect of lines upon the importance of a node. The experimental results show that our proposed NL centrality algorithm can outperform well-known centralities in terms of accuracy and efficiency.

Key words: complex network, node influence capability, neighbor node, importance of line

中图分类号:

TP301.6

刘书磊, 杜家乐, 邵增珍. 一种基于邻居节点和边的复杂网络节点排序方法——NL中心性算法[J]. 山东科学, 2019, 32(2): 130-136.

LIU Shu-lei, DU Jia-le, SHAO Zeng-zhen. An ranking method for nodes in complex networks based on neighbor nodes and the lines——NL centrality algorithm[J]. Shandong Science, 2019, 32(2): 130-136.

参考文献［18］

［1］	De DOMENICO M, SOLÉ-RIBALTA A, OMODEI E, et al. Ranking in interconnected multilayer networks reveals versatile nodes［J］. Nature Communications, 2015, 6:6868. doi: 10.1038/ncomms7868
［2］	LIU J G, REN Z M, GUO Q, et al. Node importance ranking of complex networks ［J］.Acta Phys Sinica, 2013,62(17):178901. doi: 10.7498/aps.62.178901
［3］	MOTTER A E. Cascade control and defense in complex networks ［J］.Phys Rev Lett，2004, 93 (9): 098701. doi: 10.1103/PhysRevLett.93.098701
［4］	PASTOR-SATORRAS R, VÁZQUEZ A, VESPIGNANI A. Dynamical and correlation properties of the Internet ［J］. Phys Rev Lett，2001, 87 (25): 258701. doi: 10.1103/PhysRevLett.87.258701
［5］	FREEMAN L C. Centrality in social networks conceptual clarification［J］.Social Networks，1978-1979, 1 (3): 215-239. doi: 10.1016/0378-8733(78)90021-7
［6］	FREEMAN L C. A set of measures of centrality based on betweenness ［J］.Sociometry，1977, 40 (1) : 35-41.
［7］	SABIDUSSI G. The centrality index of a graph［J］.Psychometrika，1966, 31 (4): 581-603.
［8］	CHEN D B, LÜ L Y, SHANG M S, et al. Identifying influential nodes in complex networks［J］.Physica A: Statistical Mechanics and Its Applications, 2012, 391 (4) :1777-1787. doi: 10.1016/j.physa.2011.09.017
［9］	KITSAK M, GALLOS L K, HAVLIN S, et al. Identification of influential spreaders in complex networks［J］.Nat Phys，2011,6(11):888-893. doi: 10.1038/nphys1746
［10］	ZENG A, ZHANG C J. Ranking spreaders by decomposing complex networks ［J］.Phys Lett A, 2013,377 (14): 1031-1035. doi: 10.1016/j.physleta.2013.02.039
［11］	WANG Z X, ZHAO Y, XI J K, et al. Fast ranking influential nodes in complex networks using a k-shell iteration factor［J］. Physica A: Statistical Mechanics and Its Applications, 2016, 461:171-181. doi: 10.1016/j.physa.2016.05.048
［12］	WANG Z X, DU C J, FAN J P, et al. Ranking influential nodes in social networks based on node position and neighborhood［J］. Neurocomputing, 2017, 260(18):466-477. doi: 10.1016/j.neucom.2017.04.064
［13］	WANG J Y, HOU X N, LI K Z, et al. A novel weight neighborhood centrality algorithm for identifying influential spreaders in complex networks［J］. Physica A: Statistical Mechanics and Its Applications, 2017, 475:88-105. doi: 10.1016/j.physa.2017.02.007
［14］	LIU J, XIONG Q Y, SHI W R, et al. Evaluating the importance of nodes in complex networks［J］. Physica A: Statistical Mechanics and Its Applications, 2016, 452:209-219. doi: 10.1016/j.physa.2016.02.049
［15］	LUSSEAU D, SCHNEIDER K, BOISSEAU O J, et al. The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations: Can geographic isolation explain this unique trait? ［J］. Behavioral Ecology and Sociobiology, 2003, 54(4):396-405. doi: 10.1007/S00265-003-0651-y
［16］	NEWMAN M E J. Finding community structure in networks using the eigenvectors of matrices ［J］. Physical Review E, 2006,74(3):036104. doi: 10.1103/PhysRevE.74.036104
［17］	MORENO Y, PASTOR-SATORRAS R, VESPIGNANI A. Epidemic outbreaks in complex heterogeneous networks［J］. The European Physical Journal B: Condensed Matter, 2002, 26(4):521-529. doi: 10.1140/epjb/e20020122
［18］	ZHU C, WANG X, ZHU L. A novel method of evaluating key nodes in complex networks［J］. Chaos, Solitons and Fractals, 2017, 96:43-50.DOI: doi: 10.1016/j.chaos.2017.01.007

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一种基于邻居节点和边的复杂网络节点排序方法——NL中心性算法

An ranking method for nodes in complex networks based on neighbor nodes and the lines——NL centrality algorithm

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一种基于邻居节点和边的复杂网络节点排序方法——NL中心性算法

An ranking method for nodes in complex networks based on neighbor nodes and the lines——NL centrality algorithm

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