Please wait a minute...
Chin. Phys. B, 2021, Vol. 30(8): 088901    DOI: 10.1088/1674-1056/abea86

LCH: A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks

Gui-Qiong Xu(徐桂琼)1,†, Lei Meng(孟蕾)1, Deng-Qin Tu(涂登琴)1,‡, and Ping-Le Yang(杨平乐)2,3
1 Department of Information Management, School of Management, Shanghai University, Shanghai 200444, China;
2 Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;
3 School of Electrical and Information Engineering, Jiangsu University of Science and Technology Zhangjiagang 215600, China
Abstract  Identifying influential nodes in complex networks is one of the most significant and challenging issues, which may contribute to optimizing the network structure, controlling the process of epidemic spreading and accelerating information diffusion. The node importance ranking measures based on global information are not suitable for large-scale networks due to their high computational complexity. Moreover, they do not take into account the impact of network topology evolution over time, resulting in limitations in some applications. Based on local information of networks, a local clustering H-index (LCH) centrality measure is proposed, which considers neighborhood topology, the quantity and quality of neighbor nodes simultaneously. The proposed measure only needs the information of first-order and second-order neighbor nodes of networks, thus it has nearly linear time complexity and can be applicable to large-scale networks. In order to test the proposed measure, we adopt the susceptible-infected-recovered (SIR) and susceptible-infected (SI) models to simulate the spreading process. A series of experimental results on eight real-world networks illustrate that the proposed LCH can identify and rank influential nodes more accurately than several classical and state-of-the-art measures.
Keywords:  complex networks      influential nodes      local structure      susceptible infected recovered model      susceptible infected model  
Received:  26 November 2020      Revised:  02 February 2021      Accepted manuscript online:  01 March 2021
PACS:  89.75.Fb (Structures and organization in complex systems)  
Fund: Project supported by the National Natural Foundation of China (Grant No. 11871328) and the Shanghai Science and Technology Development Funds Soft Science Research Project (Grant No. 21692109800).
Corresponding Authors:  Gui-Qiong Xu, Deng-Qin Tu     E-mail:;

Cite this article: 

Gui-Qiong Xu(徐桂琼), Lei Meng(孟蕾), Deng-Qin Tu(涂登琴), and Ping-Le Yang(杨平乐) LCH: A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks 2021 Chin. Phys. B 30 088901

[1] Watts D J and Strogatz S H 1998 Nature 393 440
[2] Newman M E J 2003 SIAM Rev. 45 167
[3] Albert R, Jeong H and Barabási A L 2000 Nature 406 378
[4] Reis S D, Hu Y Q, Babino A, Andrade J S, Canals S, Sigman M and Makse H A 2014 Nat. Phys. 10 762
[5] Schadt E E 2009 Nature 461 218
[6] Barabási A L, Gulbahce N and Loscalzo J 2010 Nat. Rev. Genet. 12 56
[7] Zhang J P, Guo H M, Jing W J and Jin Z 2019 Acta. Phys. Sin. 68 150501 (in Chinese)
[8] Wu L R, Li J J and Qi J Y 2019 Acta. Phys. Sin. 68 078901 (in Chinese)
[9] Liu F, Wang Z and Deng Y 2020 Knowl-Based Syst. 193 105464
[10] Sun Y, Yao P Y, Wan L J, Shen J and Zhong Y 2017 Chin. Phys. B 26 020201
[11] Yang P L, Xu G Q, Yu Q and Guo J W 2020 Chaos 30 093106
[12] Freeman L C 1978 Soc. Netw. 1 215
[13] Brin S and Page L 1998 Comput. Netw. ISDN Sys. 30 107
[14] Lü LY, Zhang Y C, Yeung C H and Zhou T 2011 PLoS ONE 6 e21202
[15] Kitsak M, Gallos L K, Havlin S, Liljeros F, Muchnik L, Stanley H E and Makse H A 2010 Nat. Phys. 6 888
[16] Bae J and Kim S 2014 Physica A 395 549
[17] Wang Z X, Du C J, Fan J P and Xing Y 2017 Neurocomputing 260 466
[18] Maji G 2020 J. Comput. Sci. 39 101055
[19] Hu J T, Du Y X, Mo H M, Wei D J and Deng Y 2016 Physica A 444 73
[20] Yang Y Z, Yu L, Wang X, Zhou Z L, Chen Y and Kou T 2019 Physica A 526 121118
[21] Yang P L, Liu X and Xu G Q 2018 Mod. Phys. Lett. B 32 1850216
[22] Yan X L, Cui Y P and Ni S J 2020 Chin. Phys. B 29 048902
[23] Yang Y Z, Hu M and Huang T Y 2020 Chin. Phys. B 29 088903
[24] Chen D B, Lü L Y, Shang M S, Zhang Y C and Zhou T 2012 Physica A 391 1777
[25] Chen D B, Gao H, Lü L Y and Zhou T 2013 PLoS ONE 8 e77455
[26] Gao S, Ma J, Chen Z M, Wang G H and Xing C M 2014 Physica A 403 130
[27] Li M T, Zhang R S, Hu R J, Yang F, Yao Y B and Yuan Y N 2018 Int. J Mod. Phys. B 32 1850118
[28] Berahmand K, Bouyer A and Samadi N 2018 Chaos Soliton Fract. 110 41
[29] Wang Z X, Sun C C, Yuan G, Rui X B and Yang X D 2020 J. Comput Sci. 43 101129
[30] Tang J X, Zhang R S, Yao Y B, Yang F, Zhao Z L, Hu R J and Yuan Y N 2019 Physica A 513 477
[31] Wen T and Deng Y 2020 Inform. Sci. 512 549
[32] Ruan Y R, Lao S Y, Xiao Y D, Wang J D and Bai L 2016 Chin. Phys. Lett. 33 28901
[33] Hirsch J E 2005 Proc. Natl. Acad. Sci. 102 16569
[34] Lü L Y, Zhou T, Zhang Q M and Stanley H E 2016 Nat. Commun. 7 10168
[35] Liu Q, Zhu Y X, Jia Y, Deng L, Zhou B, Zhu J X and Zou P 2018 Physica A 512 379
[36] Zareie A and Sheikhahmadi A 2019 Physica A 514 141
[37] Christakis N A and Fowler J H 2012 Stat. Med. 32 556
[38] Eguiluz V M and Klemm K 2002 Phys. Rev. Lett. 89 108701
[39] Petermann T and Rios P 2004 Phys. Rev. E 69 066116
[40] Zhou T, Yan G and Wang B H 2005 Phys. Rev. E 71 046141
[41] Wang J Y, Hou X N, Li K Z and Ding Y 2017 Physica A 475 88
[42] Castellano C and Pastor-Satorras R 2010 Phys. Rev. Lett. 105 218701
[43] May R M and Anderson R M 1979 Nature 280 455
[44] Kermack W O and McKendrick A G 1991 Bull. Math. Biol. 53 57
[45] Dorogovtsev S N, Goltsev A V and Mendes J F 2008 Rev. Mod. Phys. 80 1275
[46] Kendall M G 1938 Biometrika 30 81
[1] Analysis of cut vertex in the control of complex networks
Jie Zhou(周洁), Cheng Yuan(袁诚), Zu-Yu Qian(钱祖燏), Bing-Hong Wang(汪秉宏), and Sen Nie(聂森). Chin. Phys. B, 2023, 32(2): 028902.
[2] Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding
Pengli Lu(卢鹏丽) and Wei Chen(陈玮). Chin. Phys. B, 2023, 32(1): 018903.
[3] An extended improved global structure model for influential node identification in complex networks
Jing-Cheng Zhu(朱敬成) and Lun-Wen Wang(王伦文). Chin. Phys. B, 2022, 31(6): 068904.
[4] A novel method for identifying influential nodes in complex networks based on gravity model
Yuan Jiang(蒋沅), Song-Qing Yang(杨松青), Yu-Wei Yan(严玉为),Tian-Chi Tong(童天驰), and Ji-Yang Dai(代冀阳). Chin. Phys. B, 2022, 31(5): 058903.
[5] Characteristics of vapor based on complex networks in China
Ai-Xia Feng(冯爱霞), Qi-Guang Wang(王启光), Shi-Xuan Zhang(张世轩), Takeshi Enomoto(榎本刚), Zhi-Qiang Gong(龚志强), Ying-Ying Hu(胡莹莹), and Guo-Lin Feng(封国林). Chin. Phys. B, 2022, 31(4): 049201.
[6] Robust H state estimation for a class of complex networks with dynamic event-triggered scheme against hybrid attacks
Yahan Deng(邓雅瀚), Zhongkai Mo(莫中凯), and Hongqian Lu(陆宏谦). Chin. Phys. B, 2022, 31(2): 020503.
[7] Finite-time synchronization of uncertain fractional-order multi-weighted complex networks with external disturbances via adaptive quantized control
Hongwei Zhang(张红伟), Ran Cheng(程然), and Dawei Ding(丁大为). Chin. Phys. B, 2022, 31(10): 100504.
[8] Detection of influential nodes with multi-scale information
Jing-En Wang(王静恩), San-Yang Liu(刘三阳), Ahmed Aljmiai, and Yi-Guang Bai(白艺光). Chin. Phys. B, 2021, 30(8): 088902.
[9] Complex network perspective on modelling chaotic systems via machine learning
Tong-Feng Weng(翁同峰), Xin-Xin Cao(曹欣欣), and Hui-Jie Yang(杨会杰). Chin. Phys. B, 2021, 30(6): 060506.
[10] Exploring individuals' effective preventive measures against epidemics through reinforcement learning
Ya-Peng Cui(崔亚鹏), Shun-Jiang Ni (倪顺江), and Shi-Fei Shen(申世飞). Chin. Phys. B, 2021, 30(4): 048901.
[11] Determination of charge-compensated C3v (II) centers for Er 3+ ions in CdF2 and CaF2 crystals
Rui-Peng Chai(柴瑞鹏), Dan-Hui Hao(郝丹辉), Dang-Li Gao(高当丽), and Qing Pang(庞庆). Chin. Phys. B, 2021, 30(3): 037601.
[12] 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). Chin. Phys. B, 2020, 29(8): 088903.
[13] Identifying influential spreaders in complex networks based on entropy weight method and gravity law
Xiao-Li Yan(闫小丽), Ya-Peng Cui(崔亚鹏), Shun-Jiang Ni(倪顺江). Chin. Phys. B, 2020, 29(4): 048902.
[14] Modeling and analysis of the ocean dynamic with Gaussian complex network
Xin Sun(孙鑫), Yongbo Yu(于勇波), Yuting Yang(杨玉婷), Junyu Dong(董军宇)†, Christian B\"ohm, and Xueen Chen(陈学恩). Chin. Phys. B, 2020, 29(10): 108901.
[15] Pyramid scheme model for consumption rebate frauds
Yong Shi(石勇), Bo Li(李博), Wen Long(龙文). Chin. Phys. B, 2019, 28(7): 078901.
No Suggested Reading articles found!