| INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
Prev
Next
|
|
|
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.
|
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: xugq@staff.shu.edu.cn;shumse724@shu.edu.cn
|
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 |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|
