A novel method for identifying influential nodes in complex networks based on gravity model

doi:10.1088/1674-1056/ac4226

Abstract How to identify influential nodes in complex networks is an essential issue in the study of network characteristics. A number of methods have been proposed to address this problem, but most of them focus on only one aspect. Based on the gravity model, a novel method is proposed for identifying influential nodes in terms of the local topology and the global location. This method comprehensively examines the structural hole characteristics and K-shell centrality of nodes, replaces the shortest distance with a probabilistically motivated effective distance, and fully considers the influence of nodes and their neighbors from the aspect of gravity. On eight real-world networks from different fields, the monotonicity index, susceptible-infected-recovered (SIR) model, and Kendall's tau coefficient are used as evaluation criteria to evaluate the performance of the proposed method compared with several existing methods. The experimental results show that the proposed method is more efficient and accurate in identifying the influence of nodes and can significantly discriminate the influence of different nodes.

Keywords: influential nodes gravity model structural hole K-shell

Received: 08 November 2021
Revised: 07 December 2021
Accepted manuscript online:

PACS:	89.75.Hc	(Networks and genealogical trees)
	89.75.Fb	(Structures and organization in complex systems)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos.61663030 and 61663032).

Corresponding Authors: Song-Qing Yang,E-mail:Young_SongQ@163.com
E-mail: Young_SongQ@163.com

About author: 2021-12-11

Cite this article:

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) A novel method for identifying influential nodes in complex networks based on gravity model 2022 Chin. Phys. B 31 058903

[1] Liu Y Y, Slotine J J and Barabasi A L 2011 Nature 473 167
[2] Gao J, Zhang Y C and Zhou T 2019 Phys. Rep. 817 1
[3] Vermeulen R, Schymanski E L, Barabasi A L and Miller G W 2020 Science 367 392
[4] Liu S T and Wang P 2018 Fractal Control Theory (Singapore: Springer Nature) pp. 9-24
[5] Shi H J, Duan Z S, Chen G R and Li R 2009 Chin. Phys. B 18 03309
[6] Hosni E I A, Li K and Ahmad S 2020 Inform. Sci. 512 1458
[7] Liu S T, Zhang Y P and Liu C 2020 Fractal Control and Its Applications (Singapore: Springer Nature) pp. 129-163
[8] Yan Y W, Jiang Y, Yu R B, Yang S Q and Hong C 2021 Chin. Phys. B 31 018901
[9] Bonacich P 1972 J. Math. Sociology 2 113
[10] Freeman L C 1977 Sociometry 40 35
[11] Opsahl T, Agneessens F and Skvoretz J 2010 Social Networks 32 245
[12] Brockmann D and Helbing D 2013 Science 342 1337
[13] Kitsak M, Gallos L K, Havlin S, Liljeros F, Muchnik L, Stanley H E and Makse H A 2010 Nat. Phys. 6 888
[14] Zeng A and Zhang C J 2013 Phys. Lett. A 377 1031
[15] Bae J and Kim S 2014 Physica A 395 549
[16] Wang K L, Wu C X, Ai J and Su Z 2019 Acta Phys. Sin. 68 196402 (in Chinese)
[17] Burt R S, Kilduff M and Tasselli S. 2013 Ann. Rev. Psychol. 64 527
[18] Su X P and Song R R 2015 Acta Phys. Sin. 64 020101 (in Chinese)
[19] Yang S Q, Jiang Y, Tang T C, Yan Y W and Gan G S 2021 Acta Phys. Sin. 70 216401 (in Chinese)
[20] Li Z, Ren T, Ma X Q, Liu S M, Zhang Y X and Zhou T 2019 Sci. Rep. 9 8387
[21] Ma L L, Ma C, Zhang H F and Wang B H 2016 Physica A 451 205
[22] Yan X L, Cui Y P and Ni S J 2020 Chin. Phys. B 29 048902
[23] Lusseau D, Schneider K, Boisseau O, Haase P, Slooten E and Dawson S 2003 Behav. Ecol. Sociobiol. 54 396
[24] Girvan M and Newman M E J 2002 Proc. Nati. Acad. Sci. 99 7821
[25] Gleiser P M and Danon L 2003 Complex Syst. 06 565
[26] Colizza V, Pastor-Satorras R and Vespignani A 2007 Nat. Phys. 3 276
[27] Newman M E J 2006 Phys. Rev. E 64 036104
[28] Duch J and Arenas A 2005 Phys. Rev. E 72 027104
[29] Guimera R, Danon L, Diaz-Guilera A, Giralt F and Arenas A 2003 Phys. Rev. E 68 065103
[30] Watts D J and Strogatz S H 1998 Nature 393 440
[31] Pastor-Satorras R and Vespignani A 2001 Phys. Rev. Lett. 86 3200
[32] Knight W R 1966 J. Amer. Statist. Associat. 61 436

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