Influence of network structure on spreading dynamics via tie range

doi:10.1088/1674-1056/ad50c3

Abstract There are various phenomena of malicious information spreading in the real society, which cause many negative impacts on the society. In order to better control the spreading, it is crucial to reveal the influence of network structure on network spreading. Motifs, as fundamental structures within a network, play a significant role in spreading. Therefore, it is of interest to investigate the influence of the structural characteristics of basic network motifs on spreading dynamics. Considering the edges of the basic network motifs in an undirected network correspond to different tie ranges, two edge removal strategies are proposed, short ties priority removal strategy and long ties priority removal strategy. The tie range represents the second shortest path length between two connected nodes. The study focuses on analyzing how the proposed strategies impact network spreading and network structure, as well as examining the influence of network structure on network spreading. Our findings indicate that the long ties priority removal strategy is most effective in controlling network spreading, especially in terms of spread range and spread velocity. In terms of network structure, the clustering coefficient and the diameter of network also have an effect on the network spreading, and the triangular structure as an important motif structure effectively inhibits the spreading.

Keywords: network spreading network motifs tie range edge removal strategy

Received: 16 April 2024
Revised: 24 May 2024
Accepted manuscript online:

PACS:	89.75.Fb	(Structures and organization in complex systems)
	05.90.+m	(Other topics in statistical physics, thermodynamics, and nonlinear dynamical systems)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 62373197 and 62203229) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (Grant No. KYCX24_1211).

Corresponding Authors: Guo-Ping Jiang
E-mail: jianggp@njupt.edu.cn

Cite this article:

Min Li(李敏), Yurong Song(宋玉蓉), Bo Song(宋波), Ruqi Li(李汝琦), Guo-Ping Jiang(蒋国平), and Zhang Hui(张晖) Influence of network structure on spreading dynamics via tie range 2024 Chin. Phys. B 33 088902

[1] Tudisco F and Higham D J 2021 Commun. Phys. 4 201
[2] Molaei S, Farahbakhsh R, Salehi M and Crespi N 2020 Expert Syst. Appl. 160 113580
[3] Ouyang B, Xia Y X, Wang C, et al. 2018 IEEE Trans. Circuits Syst. II Express Briefs 65 1244-1248
[4] Ma Z W, Sun L, Ding Z G, Huang Y Z and Hu Z L 2024 Chinese Phys. B 33 028902
[5] Milo R, Shen-Orr S, Itzkovitz S, et al. 2002 Science 298 824-827
[6] Bianconi G and Capocci A 2003 Phys. Rev. Lett. 90 078701
[7] Kim H J and Kim J M 2005 Phys. Rev. E 72 036109
[8] Jiang S Y, Zhou J, Small M, et al. 2023 Phys. Rev. Lett. 130 187402
[9] Battiston F, Cencetti G, Iacopini I, et al. 2020 Phys. Rep. 874 1-92
[10] Shi D H, Chen Z F, Sun X, et al. 2021 Commun. Phys. 4 249
[11] Zhang L F, Su C, Jin Y F, et al. 2018 Inform. Sciences 451-452 240-252
[12] Pan L M, Wang W, Tian L X and Lai Y C 2021 Phys. Rev. E 103 012302
[13] Zhong Z J, Yang Y K, Miao R Q, Peng Y Q and Liu G Y 2022 Chin. Phys. B 31 040205
[14] Zheng J B, Gao M, Lim E P, et al. 2022 Knowl. Inf. Syst. 64 1967
[15] Zheng K X, Liu Y, Gong J and Wang W 2022 Chaos, Solitons and Fractals 157 111934
[16] Chandrasekhar K, Kadelka C, Laubenbacher R and Murrugarra D 2023 Physica D 451 133775
[17] Meena C, Hens C, Acharyya S, et al. 2023 Nat. Phys. 19 1033
[18] Kang X J, Li X C, Yao H, et al. 2022 Inform. Sciences 616 37
[19] Harling G and Onnela J P 2018 Netw. Sci. 6 34
[20] Nian F Z and Liu J S 2021 Chaos, Solitons and Fractals 149 111065
[21] Liu Y, Zeng Q, Pan L M and Tang M 2023 IEEE Trans. Netw. Sci. Eng. 10 2201-2211
[22] Watts D J and Strogatz S H 1998 Nature 393 440
[23] Barabási A L and Albert R 1999 Science 286 509
[24] Newman M E J 2003 Siam Rev. 45 167
[25] Boccaletti S, Latora V, Moreno Y, et al. 2006 Phys. Rep. 424 175
[26] Girvan M and Newman M E J 2002 Proc. Natl. Acad. Sci. USA 99 7821
[27] Nematzadeh A, Ferrara E, Flammini A and Ahn Y Y 2014 Phys. Rev. Lett. 113 259901
[28] Kates-Harbeck J and Desai M M 2023 Phys. Rev. E 108 024306
[29] Mao K 2015 Comput. Sci. 42 85
[30] Bao X G, Hu Q T, Ji P, et al. 2022 Nat. Commun. 13 5301
[31] Shen-Orr S, Milo R, Mangan S and Alon U 2002 Nat. Genet. 31 64
[32] Menck P J, Heitzig J, Marwan N and Kurths J 2013 Nat. Phys. 9 89
[33] Park P S, Blumenstock J and Macy M 2018 Science 362 1410-1413
[34] Lyu D, Yuan Y, Wang L, Wang X F and Pentland A 2022 Commun. Phys. 5 87
[35] Song J H 2023 Chin. Phys. B 32 098901
[36] Li C, van de Bovenkamp R and Van Mieghem P 2012 Phys. Rev. E 86 026116
[37] Xie Y K and Wang Z 2021 Math. Comput. Simulat. 188 23-34
[38] Chakrabarti D, Wang Y, Wang C X, et al. 2008 ACM Trans. Inf. Syst. Secur. 10 1-26
[39] Gómez S, Díza-Guilera A, Gómez-Gardeñes J, et al. 2013 Phys. Rev. Lett. 110 028701
[40] Mao G Y and Zhang N 2017 Physica A 466 243-248
[41] Pan Z F, Wang X F and Li X 2006 J. Simul. 18 2346-2348
[42] Mo Y Q, Dasgupta S and Jacob B 2023 IEEE T. Automat. Contr. 68 454-461

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Influence of network structure on spreading dynamics via tie range

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