The relative importance of structure and dynamics on node influence in reversible spreading processes

Jun-Yi Qu, Ming Tang, Ying Liu, Shu-Guang Guan

PDF(1512 KB)
PDF(1512 KB)
Front. Phys. ›› 2021, Vol. 16 ›› Issue (5) : 51503. DOI: 10.1007/s11467-021-1082-9
RESEARCH ARTICLE
RESEARCH ARTICLE

The relative importance of structure and dynamics on node influence in reversible spreading processes

Author information +
History +

Abstract

The reversible spreading processes with repeated infection widely exist in nature and human society, such as gonorrhea propagation and meme spreading. Identifying influential spreaders is an important issue in the reversible spreading dynamics on complex networks, which has been given much attention. Except for structural centrality, the nodes’ dynamical states play a significant role in their spreading influence in the reversible spreading processes. By integrating the number of outgoing edges and infection risks of node’s neighbors into structural centrality, a new measure for identifying influential spreaders is articulated which considers the relative importance of structure and dynamics on node influence. The number of outgoing edges and infection risks of neighbors represent the positive effect of the local structural characteristic and the negative effect of the dynamical states of nodes in identifying influential spreaders, respectively. We find that an appropriate combination of these two characteristics can greatly improve the accuracy of the proposed measure in identifying the most influential spreaders. Notably, compared with the positive effect of the local structural characteristic, slightly weakening the negative effect of dynamical states of nodes can make the proposed measure play the best performance. Quantitatively understanding the relative importance of structure and dynamics on node influence provides a significant insight into identifying influential nodes in the reversible spreading processes.

Graphical abstract

Keywords

reversible spreading process / node influence / local structure / dynamical state

Cite this article

Download citation ▾
Jun-Yi Qu, Ming Tang, Ying Liu, Shu-Guang Guan. The relative importance of structure and dynamics on node influence in reversible spreading processes. Front. Phys., 2021, 16(5): 51503 https://doi.org/10.1007/s11467-021-1082-9

References

[1]
D. Koschützki, K. A. Lehmann, L. Peeters, S. Richter, D. Tenfelde-Podehl, and O. Zlotowski,in: Network Analysis, Springer, 2005, pp 16–61
CrossRef ADS Google scholar
[2]
L. Lü, D. Chen, X. Ren, Q. Zhang, Y. Zhang, and T. Zhou, Vital nodes identification in complex networks, Phys. Rep. 650, 1 (2016)
CrossRef ADS Google scholar
[3]
S. Pei, J. Wang, F. Morone, and H. A. Makse, Influencer identification in dynamical complex systems, J. Complex Netw. 8(2), cnz029 (2020)
CrossRef ADS Google scholar
[4]
J. Leskovec, L. A. Adamic, and B. A. Huberman, The dynamics of viral marketing, ACM Trans. Web 1(1), 5 (2007)
CrossRef ADS Google scholar
[5]
A. Bovet and H. A. Makse, Influence of fake news in Twitter during the 2016 US presidential election, Nat. Commun. 10(1), 1 (2019)
CrossRef ADS Google scholar
[6]
Y. T. Lin, X. P. Han, B. K. Chen, J. Zhou, and B. H. Wang, Evolution of innovative behaviors on scale-free networks, Front. Phys. 13(4), 130308 (2018)
CrossRef ADS Google scholar
[7]
A. E. Motter and Y. Lai, Cascade-based attacks on complex networks, Phys. Rev. E 66(6), 065102 (2002)
CrossRef ADS Google scholar
[8]
R. Albert, I. Albert, and G. L. Nakarado, Structural vulnerability of the North American power grid, Phys. Rev. E 69(2), 025103 (2004)
CrossRef ADS Google scholar
[9]
R. Pastor-Satorras and A. Vespignani, Immunization of complex networks, Phys. Rev. E 65(3), 036104 (2002)
CrossRef ADS Google scholar
[10]
S. V. Scarpino and G. Petri, On the predictability of infectious disease outbreaks,Nat. Commun . 10(1), 898 (2019)
CrossRef ADS Google scholar
[11]
J. Zhou and Z. H. Liu, Epidemic spreading in complex networks, Front. Phys. 3(3), 331 (2008)
CrossRef ADS Google scholar
[12]
F. Morone and H. A. Makse, Influence maximization in complex networks through optimal percolation, Nature 524(7563), 65 (2015)
CrossRef ADS Google scholar
[13]
S. Pei, F. Morone, and H. A. Makse, in: Complex Spreading Phenomena in Social Systems, Springer, 2018, pp 125–148
CrossRef ADS Google scholar
[14]
Y. Hu, S. Ji, Y. Jin, L. Feng, H. E. Stanley, and S. Havlin, Local structure can identify and quantify influential global spreaders in large scale social networks, Proc. Natl. Acad. Sci. USA 115(29), 7468 (2018)
CrossRef ADS Google scholar
[15]
A. Y. Lokhov and D. Saad, Optimal deployment of resources for maximizing impact in spreading processes, Proc. Natl. Acad. Sci. USA 114(39), E8138 (2017)
CrossRef ADS Google scholar
[16]
K. Zheng, Y. Liu, Y. Wang, and W. Wang, k-core percolation on interdependent and interconnected multiplex networks, arXiv: 2101.02335 (2021)
CrossRef ADS Google scholar
[17]
G. Poux-Médard, R. Pastor-Satorras, and C. Castellano, Influential spreaders for recurrent epidemics on networks, Phys. Rev. Res.2(2), 023332 (2020)
CrossRef ADS Google scholar
[18]
S. Erkol, D. Mazzilli, and F. Radicchi, Influence maximization on temporal networks, Phys. Rev. E 102(4), 042307 (2020)
CrossRef ADS Google scholar
[19]
S. Aral and P. S. Dhillon, Social influence maximization under empirical influence models, Nat. Hum. Behav. 2(6), 375 (2018)
CrossRef ADS Google scholar
[20]
K. Klemm, M. Á. Serrano, V. M. Eguíluz, and M. S. Miguel, A measure of individual role in collective dynamics, Sci. Rep. 2(1), 1 (2012)
CrossRef ADS Google scholar
[21]
J. P. Gleeson, J. A. Ward, K. P. Osullivan, and W. T. Lee, Competition-induced criticality in a model of meme popularity, Phys. Rev. Lett. 112(4), 048701 (2014)
CrossRef ADS Google scholar
[22]
R. Pastor-Satorras and A. Vespignani, Epidemic dynamics and endemic states in complex networks, Phys. Rev. E 63(6), 066117 (2001)
CrossRef ADS Google scholar
[23]
S. K. Stavroglou, A. A. Pantelous, H. E. Stanley, and K. M. Zuev, Hidden interactions in financial markets, Proc. Natl. Acad. Sci. USA 116(22), 10646 (2019)
CrossRef ADS Google scholar
[24]
B. Barzel and A. Barabási, Universality in network dynamics, Nat. Phys. 9(10), 673 (2013)
CrossRef ADS Google scholar
[25]
R. Pastor-Satorras, and A. Vespignani, Epidemic Spreading in Scale-Free Networks, Phys. Rev. Lett. 86(14), 3200 (2001)
CrossRef ADS Google scholar
[26]
R. Pastor-Satorras, and A. Vespignani, Epidemic dynamics and endemic states in complex networks, Phys. Rev. E 63(6), 066117 (2001)
CrossRef ADS Google scholar
[27]
J. Qu, M. Tang, Y. Liu, and S. Guan, Identifying influential spreaders in reversible process, Chaos Solitons Fractals 140, 110197 (2020)
CrossRef ADS Google scholar
[28]
P. Shu, W. Wang, M. Tang, P. Zhao, and Y. Zhang, Recovery rate affects the effective epidemic threshold with synchronous updating, Chaos 26(6), 063108 (2016)
CrossRef ADS Google scholar
[29]
Y. Liu, M. Tang, T. Zhou, and Y. Do, Core-like groups result in invalidation of identifying super-spreader by kshell decomposition, Sci. Rep. 5(1), 9602 (2015)
CrossRef ADS Google scholar
[30]
S. C. Ferreira, C. Castellano, and R. Pastor-Satorras, Epidemic thresholds of the susceptible-infected-susceptible model on networks: A comparison of numerical and theoretical results, Phys. Rev. E 86(4), 041125 (2012)
CrossRef ADS Google scholar
[31]
P. Shu, W. Wang, M. Tang, and Y. Do, Numerical identification of epidemic thresholds for susceptible-infectedrecovered model on finite-size networks, Chaos 25(6), 063104 (2015)
CrossRef ADS Google scholar
[32]
Y. Xu, M. Tang, Y. Liu, Y. Zou, and Z. Liu, Identifying epidemic threshold by temporal profile of outbreaks on networks, Chaos 29(10), 103141 (2019)
CrossRef ADS Google scholar
[33]
Y. Liu, M. Tang, T. Zhou, and Y. Do, Identify influential spreaders in complex networks, the role of neighborhood, Physica A 452, 289 (2016)
CrossRef ADS Google scholar
[34]
M. G. Kendall, A new measure of rank correlation, Biometrika 30(1–2), 81 (1938)
CrossRef ADS Google scholar
[35]
M. E. Newman, Finding community structure in networks using the eigenvectors of matrices, Phys. Rev. E 74(3), 036104 (2006)
CrossRef ADS Google scholar
[36]
N. Spring, R. Mahajan, and D. Wetherall, Measuring ISP topologies with rocketfuel, Comput. Commun. Rev. 32(4), 133 (2002)
CrossRef ADS Google scholar
[37]
M. Boguñá, R. Pastorsatorras, A. Diazguilera, and A. Arenas, Models of social networks based on social distance attachment, Phys. Rev. E 70(5), 056122 (2004)
CrossRef ADS Google scholar
[38]
M. E. Newman, The structure of scientific collaboration networks, Proc. Natl. Acad. Sci. USA 98(2), 404 (2001)
CrossRef ADS Google scholar
[39]
M. Kitsak, L. K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. E. Stanley, and H. A. Makse, Identification of influential spreaders in complex networks, Nat. Phys. 6(11), 888 (2010)
CrossRef ADS Google scholar
[40]
M. Boguñá, C. Castellano, and R. Pastor-Satorras, Nature of the epidemic threshold for the susceptible-infectedsusceptible dynamics in networks, Phys. Rev. Lett. 111(6), 068701 (2013)
CrossRef ADS Google scholar
[41]
C. Castellano and R. Pastor-Satorras, Competing activation mechanisms in epidemics on networks, Sci. Rep. 2(1), 371 (2012)
CrossRef ADS Google scholar
[42]
H. Zhang, J. Xie, M. Tang, and Y. Lai, Suppression of epidemic spreading in complex networks by local information based behavioral responses, Chaos 24(4), 043106 (2014)
CrossRef ADS Google scholar
[43]
X. Chen, R. Wang, M. Tang, S. Cai, H. E. Stanley, and L. A. Braunstein, Suppressing epidemic spreading in multiplex networks with social-support, New J. Phys. 20(1), 013007 (2018)
CrossRef ADS Google scholar
[44]
W. Wang, M. Tang, H. Zhang, and Y. Lai, Dynamics of social contagions with memory of nonredundant information, Phys. Rev. E 92(1), 012820 (2015)
CrossRef ADS Google scholar
[45]
Z. Lin, M. Feng, M. Tang, Z. Liu, C. Xu, P. M. Hui, and Y. Lai, Non-Markovian recovery makes complex networks more resilient against largescale failures, Nat. Commun. 11, 2490 (2020)
CrossRef ADS Google scholar

RIGHTS & PERMISSIONS

2021 Higher Education Press
AI Summary AI Mindmap
PDF(1512 KB)

Accesses

Citations

Detail

Sections
Recommended

/