Please wait a minute...
Chin. Phys. B, 2022, Vol. 31(6): 068901    DOI: 10.1088/1674-1056/ac4484
Special Issue: SPECIAL TOPIC— Interdisciplinary physics: Complex network dynamics and emerging technologies
SPECIAL TOPIC—Interdisciplinary physics: Complex network dynamics and emerging technologies Prev   Next  

Influence fast or later: Two types of influencers in social networks

Fang Zhou(周方)1, Chang Su(苏畅)1, Shuqi Xu(徐舒琪)1, and Linyuan Lü(吕琳媛)1,2,†
1 Yangtze Delta Region Institute(Huzhou)&Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Huzhou 313001, China;
2 Beijing Computational Science Research Center, Beijing 100193, China
Abstract  In real-world networks, there usually exist a small set of nodes that play an important role in the structure and function of networks. Those vital nodes can influence most of other nodes in the network via a spreading process. While most of the existing works focused on vital nodes that can maximize the spreading size in the final stage, which we call final influencers, recent work proposed the idea of fast influencers, which emphasizes nodes' spreading capacity at the early stage. Despite the recent surge of efforts in identifying these two types of influencers in networks, there remained limited research on untangling the differences between the fast influencers and final influencers. In this paper, we firstly distinguish the two types of influencers: fast-only influencers and final-only influencers. The former is defined as individuals who can achieve a high spreading effect at the early stage but lose their superiority in the final stage, and the latter are those individuals that fail to exhibit a prominent spreading performance at the early stage but influence a large fraction of nodes at the final stage. Further experiments are based on eight empirical datasets, and we reveal the key differences between the two types of influencers concerning their spreading capacity and the local structures. We also analyze how network degree assortativity influences the fraction of the proposed two types of influencers. The results demonstrate that with the increase of degree assortativity, the fraction of the fast-only influencers decreases, which indicates that more fast influencers tend to keep their superiority at the final stage. Our study provides insights into the differences and evolution of different types of influencers and has important implications for various empirical applications, such as advertisement marketing and epidemic suppressing.
Keywords:  social networks      fast influencers      final influencers      spreading dynamics      degree assortativity  
Received:  31 July 2021      Revised:  24 November 2021      Accepted manuscript online:  18 December 2021
PACS:  89.75.Fb (Structures and organization in complex systems) (Networks)  
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61673150 and 11622538) and Special Project for the Central Guidance on Local Science and Technology Development of Sichuan Province, China (Project No. 2021ZYD0029).
Corresponding Authors:  Linyuan Lü      E-mail:

Cite this article: 

Fang Zhou(周方), Chang Su(苏畅), Shuqi Xu(徐舒琪), and Linyuan Lü(吕琳媛) Influence fast or later: Two types of influencers in social networks 2022 Chin. Phys. B 31 068901

[1] Watts D J and Dodds P S 2007 J. Consumer Res. 34 441
[2] Kitsak M, Gallos L K, Havlin S, Liljeros F, Muchnik L, Stanley H E and Makse H A 2010 Nat. Phys. 6 888
[3] Chen D, Lü L, Shang M S, Zhang Y C and Zhou T 2012 Physica A 391 1777
[4] Morone F and Makse H A 2015 Nature 524 65
[5] Lü L, Chen D, Ren X L, Zhang Q M, Zhang Y C and Zhou T 2016 Phys. Reports 650 1
[6] Malliaros F D, Rossi M E G and Vazirgiannis M 2016 Sci. Reports 6 1
[7] Zhou F, Lü L and Mariani M S 2019 Commun. Nonlinear Sci. Numer. Simul. 74 69
[8] Wang K L, Wu C X, Ai J and Su Z 2019 Acta Phys. Sin. 68 235 (in Chinese)
[9] Pei S, Wang J, Morone F and Makse H A 2020 J. Complex Networks 8 cnz029
[10] Han W T, Yi P, Ma H L, Zhang P and Tian L 2019 Acta Phys. Sin. 68 222 (in Chinese)
[11] Newman M 2010 Networks (New York: Oxford University Press)
[12] Hirsch J E 2005 Proc. Natl. Acad. Sci. USA 102 16569
[13] Bonacich P and Lloyd P 2001 Soc. Networks 23 191
[14] Bonacich P 1972 J. Math. Sociol. 2 113
[15] Brandes U 2001 J. Math. Sociol. 25 163
[16] Freeman L C 1978 Soc. Networks 1 215
[17] Liu J G, Lin J H, Guo Q and Zhou T 2016 Sci. Rep. 6 1
[18] Brin S and Page L 1998 Comput. Networks ISDN Syst. 30 107
[19] Lü L, Zhang Y C, Yeung C H and Zhou T 2011 PloS One 6 e21202
[20] Kleinberg J M 1999 J. ACM (JACM) 46 604
[21] Lempel R and Moran S 2000 Comput. Networks 33 387
[22] Chen D B, Gao H, Lü L and Zhou T 2013 PloS One 8 e77455
[23] Eguiluz V M and Klemm K 2002 Phys. Rev. Lett. 89 108701
[24] Volchenkov D, Volchenkova L and Blanchard P 2002 Phys. Rev. E 66 046137
[25] Boguná M, Pastor-Satorras R and Vespignani A 2003 Phys. Rev. Lett. 90 028701
[26] Newman M E 2002 Phys. Rev. Lett. 89 208701
[27] Newman M E 2003 Phys. Rev. E 67 026126
[28] Xulvi-Brunet R and Sokolov I M 2004 Phys. Rev. E 70 066102
[29] Xulvi-Brunet R and Sokolov I M 2005 Acta Phys. Polonica B 36 1431
[30] Menche J, Valleriani A and Lipowsky R 2010 Phys. Rev. E 81 046103
[31] Lü L, Zhou T, Zhang Q M and Stanley H E 2016 Nat. Commun. 7 1
[32] Guimera R, Danon L, Diaz-Guilera A, Giralt F and Arenas A 2003 Phys. Rev. E 68 065103
[33] Leskovec J, Kleinberg J and Faloutsos C 2007 ACM Transactions on Knowl. Discov. from Data 1 2
[34] Rozemberczki B and Sarkar R 2020 Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models Proceedings of the 29th ACM International Conference on Information and Knowledge Management pp. 1325-1334
[35] Rozemberczki B, Allen C and Sarkar R 2021 J. Complex Networks 9 cnab014
[36] Rozemberczki B, Davies R, Sarkar R and Sutton C 2019 Gemsec: Graph embedding with self clustering Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining pp. 65-72
[37] Viswanath B, Mislove A, Cha M and Gummadi K P 2009 On the evolution of user interaction in facebook Proceedings of the 2nd ACM Workshop on Online Social Networks pp. 37-42
[38] Pastor-Satorras R, Castellano C, Van Mieghem P and Vespignani A 2015 Rev. Mod. Phys. 87 925
[39] Iannelli F, Mariani M S and Sokolov I M 2018 Phys. Rev. E 98 062302
[1] Uncovering offline event similarity of online friends by constructing null models
Wenkuo Cui(崔文阔), Jing Xiao(肖婧), Ting Li(李婷), Xiaoke Xu(许小可). Chin. Phys. B, 2019, 28(6): 068901.
[2] Evolutionary prisoner's dilemma on Newman--Watts socialnetworks with an asymmetric payoff distribution mechanism
Du Wen-Bo(杜文博), Cao Xian-Bin(曹先彬), Yang Han-Xin(杨涵新), and Hu Mao-Bin(胡茂彬) . Chin. Phys. B, 2010, 19(1): 010204.
No Suggested Reading articles found!