Influential nodes identification in complex networks based on global and local information

doi:10.1088/1674-1056/ab969f

中国物理B ›› 2020, Vol. 29 ›› Issue (8): 88903-088903.doi: 10.1088/1674-1056/ab969f

• SPECIAL TOPIC—Ultracold atom and its application in precision measurement • 上一篇下一篇

Influential nodes identification in complex networks based on global and local information

Yuan-Zhi Yang(杨远志), Min Hu(胡敏), Tai-Yu Huang(黄泰愚)

1 Air Force Engineering University, Xi'an 710038, China;
2 China Petroleum Planning and Engineering Institute, Beijing 100083, China;
3 Sichuan University of Arts and Science, Dazhou 635000, China

收稿日期:2020-04-23 修回日期:2020-05-18 出版日期:2020-08-05 发布日期:2020-08-05
通讯作者: Min Hu, Min Hu E-mail:hu_min_min@163.com;1097762865@qq.com

Influential nodes identification in complex networks based on global and local information

Yuan-Zhi Yang(杨远志)¹, Min Hu(胡敏)², Tai-Yu Huang(黄泰愚)³

1 Air Force Engineering University, Xi'an 710038, China;
2 China Petroleum Planning and Engineering Institute, Beijing 100083, China;
3 Sichuan University of Arts and Science, Dazhou 635000, China

Received:2020-04-23 Revised:2020-05-18 Online:2020-08-05 Published:2020-08-05
Contact: Min Hu, Min Hu E-mail:hu_min_min@163.com;1097762865@qq.com

摘要/Abstract

摘要： Identifying influential nodes in complex networks is essential for network robust and stability, such as viral marketing and information control. Various methods have been proposed to define the influence of nodes. In this paper, we comprehensively consider the global position and local structure to identify influential nodes. The number of iterations in the process of k-shell decomposition is taken into consideration, and the improved k-shell decomposition is then put forward. The improved k-shell decomposition and degree of target node are taken as the benchmark centrality, in addition, as is well known, the effect between node pairs is inversely proportional to the shortest path length between two nodes, and then we also consider the effect of neighbors on target node. To evaluate the performance of the proposed method, susceptible-infected (SI) model is adopted to simulate the spreading process in four real networks, and the experimental results show that the proposed method has obvious advantages over classical centrality measures in identifying influential nodes.

关键词: complex networks, influential nodes, global position, local structure, susceptible-infected (SI) model

Abstract: Identifying influential nodes in complex networks is essential for network robust and stability, such as viral marketing and information control. Various methods have been proposed to define the influence of nodes. In this paper, we comprehensively consider the global position and local structure to identify influential nodes. The number of iterations in the process of k-shell decomposition is taken into consideration, and the improved k-shell decomposition is then put forward. The improved k-shell decomposition and degree of target node are taken as the benchmark centrality, in addition, as is well known, the effect between node pairs is inversely proportional to the shortest path length between two nodes, and then we also consider the effect of neighbors on target node. To evaluate the performance of the proposed method, susceptible-infected (SI) model is adopted to simulate the spreading process in four real networks, and the experimental results show that the proposed method has obvious advantages over classical centrality measures in identifying influential nodes.

Key words: complex networks, influential nodes, global position, local structure, susceptible-infected (SI) model

中图分类号: (Structures and organization in complex systems)

89.75.Fb

杨远志, 胡敏, 黄泰愚. Influential nodes identification in complex networks based on global and local information[J]. 中国物理B, 2020, 29(8): 88903-088903.

Yuan-Zhi Yang(杨远志), Min Hu(胡敏), Tai-Yu Huang(黄泰愚). Influential nodes identification in complex networks based on global and local information[J]. Chin. Phys. B, 2020, 29(8): 88903-088903.

参考文献 31

[1]	Shao P and Chen H 2019 IEEE Access 7 118509 doi: 10.1109/ACCESS.2019.2932571
[2]	Arularasan A N, Suresh A and Seerangan K 2019 Cluster Computing 22 4035 doi: 10.1007/s10586-018-2616-y
[3]	Shukla A, Bhattacharyya A, Kuppusamy L, Srivas M and Thattai M 2017 Plos One 12 e0180692
[4]	Mou J, Liu C, Chen S, Huang G and Lu X 2017 Sci. Rep. 7 1275 doi: 10.1038/s41598-017-01380-5
[5]	Tan Z Z, Asad J H and Owaidat M Q 2017 Int. J. Circuit Theor. Appl. 45 1942 doi: 10.1002/cta.2366
[6]	Owaidat M Q, Al-Badawi A A, Asad J H and Al-Twiessi Mohammed 2018 Chin. Phys. Lett. 35 020502 doi: 10.1088/0256-307X/35/2/020502
[7]	Chasman D, Siahpirani A F and Roy S 2016 Current Opinion in Biotechnology 39 157 doi: 10.1016/j.copbio.2016.04.007
[8]	Yuan M, Hong W and Li P 2017 Biochemical Society Transactions 45 1015 doi: 10.1042/BST20160419
[9]	Bonacich P 1972 Journal of Mathematical Sociology 2 113 doi: 10.1080/0022250X.1972.9989806
[10]	Freeman L C 1978 Social Networks 1 215 doi: 10.1016/0378-8733(78)90021-7
[11]	Newman M E J 2005 Social Networks 27 39 doi: 10.1016/j.socnet.2004.11.009
[12]	Bonacich P and Lloyd P 2001 Social Networks 23 191 doi: 10.1016/S0378-8733(01)00038-7
[13]	Chen D, Lü L, Shang M S, Zhang Y C and Zhou T 2012 Physica A 391 1777 doi: 10.1016/j.physa.2011.09.017
[14]	Kistak M, Gallos L K, Havlin S, Liljeros F, Muchnik L, Stanley H E and Makse H A 2010 Nat. Phys. 6 888 doi: 10.1038/nphys1746
[15]	Zeng A and Zhang C J 2013 Phys. Lett. A 377 1031 doi: 10.1016/j.physleta.2013.02.039
[16]	Lin J H, Guo Q, Dong W Z, Tang L Y and Liu J G 2014 Phys. Lett. A 378 3279 doi: 10.1016/j.physleta.2014.09.054
[17]	Arasu A, Cho J, Garcia-Molina H, Paepcke A and Raghavan S 2001 ACM Transactions on Internet Technology 1 2 doi: 10.1145/383034.383035
[18]	Lü L, Zhang Y C, Yeung C H and Zhou T 2011 Plos One 6 e21202
[19]	Kleinberg J M 1999 J. ACM 46 604 doi: 10.1145/324133.324140
[20]	Liu Z, Jiang C, Wang J and Yu H 2015 Knowledge-Based Systems 84 56 doi: 10.1016/j.knosys.2015.03.026
[21]	Yang Y, Yu L, Wang X, Zhou Z, Chen Y and Kou T 2019 Physica A 526 121118 doi: 10.1016/j.physa.2019.121118
[22]	Mo H, Gao C and Deng Y 2015 Journal of Systems Engineering and Electronics 26 381 doi: 10.1109/JSEE.2015.00044
[23]	Ibnoulouafi A, Haziti M E and Cherifi H 2018 Journal of Statistical Mechanics Theory and Experiment 7 073407
[24]	Wang Z, Du C, Fan J and Xing Y 2017 Neurocomputing 260 466 doi: 10.1016/j.neucom.2017.04.064
[25]	Zhao X, Liu F, Wang J and Li T 2017 International Journal of Geo-Information 6 35 doi: 10.3390/ijgi6020035
[26]	Zhou T, Liu J G, Bai W J, Chen G and Wang B H 2006 Phys. Rev. E 74 056109 doi: 10.1103/PhysRevE.74.056109
[27]	Zachary W W 1977 Journal of Anthropological Research 33 452 doi: 10.1086/jar.33.4.3629752
[28]	Gleiser P M and Danon L 2003 Advances in Complex Systems 6 565 doi: 10.1142/S0219525903001067
[29]	Guimerá R, Danon L, Díaz-Guilera A, Giralt F and Arenas A 2003 Phys. Rev. E 68 065103 doi: 10.1103/PhysRevE.68.065103
[30]	Kendall M G 1938 Biometrika 30 81 doi: 10.1093/biomet/30.1-2.81
[31]	Bae J and Kim S 2014 Physica A 395 549 doi: 10.1016/j.physa.2013.10.047

Influential nodes identification in complex networks based on global and local information

Influential nodes identification in complex networks based on global and local information

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