Subtle role of latency for information diffusion in online social networks

doi:10.1088/1674-1056/25/10/108904

Abstract Information diffusion in online social networks is induced by the event of forwarding information for users, and latency exists widely in user spreading behaviors. Little work has been done to reveal the effect of latency on the diffusion process. In this paper, we propose a propagation model in which nodes may suspend their spreading actions for a waiting period of stochastic length. These latent nodes may recover their activity again. Meanwhile, the mechanism of forwarding information is also introduced into the diffusion model. Mean-field analysis and numerical simulations indicate that our model has three nontrivial results. First, the spreading threshold does not correlate with latency in neither homogeneous nor heterogeneous networks, but depends on the spreading and refractory parameter. Furthermore, latency affects the diffusion process and changes the infection scale. A large or small latency parameter leads to a larger final diffusion extent, but the intrinsic dynamics is different. Large latency implies forwarding information rapidly, while small latency prevents nodes from dropping out of interactions. In addition, the betweenness is a better descriptor to identify influential nodes in the model with latency, compared with the coreness and degree. These results are helpful in understanding some collective phenomena of the diffusion process and taking measures to restrain a rumor in social networks.

Keywords: information diffusion node latency user behavior complex networks

Received: 14 April 2016
Revised: 19 May 2016
Accepted manuscript online:

PACS:	89.75.-k	(Complex systems)
	87.23.Ge	(Dynamics of social systems)
	89.75.Fb	(Structures and organization in complex systems)
	05.10.-a	(Computational methods in statistical physics and nonlinear dynamics)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61401015 and 61271308), the Fundamental Research Funds for the Central Universities, China (Grant No. 2014JBM018), and the Talent Fund of Beijing Jiaotong University, China (Grant No. 2015RC013).

Corresponding Authors: Fei Xiong
E-mail: xiongf@bjtu.edu.cn

Cite this article:

Fei Xiong(熊菲), Xi-Meng Wang(王夕萌), Jun-Jun Cheng(程军军) Subtle role of latency for information diffusion in online social networks 2016 Chin. Phys. B 25 108904

[1]	Barthélemy M, Barrat A, Pastor-Satorras R and Vespignani A 2004 Phys. Rev. Lett. 92 178701
[2]	Nekoveea M, Morenob Y, Bianconic G and Marsili M 2007 Physica A 374 457
[3]	Lu Y, Jiang G and Song Y 2012 Chin. Phys. B 21 100207
[4]	Genois M, Vestergaard C L, Cattuto C and Barrat A 2015 Nat. Commun. 6 8860
[5]	Gong Y, Song Y and Jiang G 2012 Chin. Phys. B 21 010205
[6]	Reppas A I, De Decker Y and Siettos C I 2012 J. Stat. Mech. 2012 P08020
[7]	Zhou T, Liu J, Bai W, Chen G and Wang B 2006 Phys. Rev. E 74 056109
[8]	Barthelemy M, Barrat A, Pastor-Satorras R and Vespignani A 2002 Phys. Rev. Lett. 92 178701
[9]	Parshani R, Carmi S and Havlin S 2010 Phys. Rev. Lett. 104 258701
[10]	Newman M E J 2002 Phys. Rev. E 66 016128
[11]	Silva R D and Fernandes H A 2015 J. Stat. Mech. 2015 P06011
[12]	Wang Y, Cao J D, Alofi A, AL-Mazrooei A and Elaiw A 2015 Physica A 437 75
[13]	Castellano C, Fortunato S and Loreto V 2009 Rev. Mod. Phys. 81 591
[14]	Fotouhi B and Shirkoohi M K 2016 Phys. Rev. E 93 012301
[15]	Liu C, Xie J, Chen H, Zhang H and Tang M 2015 Chaos 25 103111
[16]	Li W, Tang S, Fang W, Guo Q, Zhang X and Zheng Z 2015 Phys. Rev. E 92 042810
[17]	Yagan O, Qian D, Zhang J and Cochran D 2013 IEEE J. Sel. Area Comm. 31 1038
[18]	Nematzadeh A, Ferrara E, Flammini A and Ahn Y Y 2014 Phys. Rev. Lett. 113 088701
[19]	Song B, Jiang G, Song Y and Xia L 2015 Chin. Phys. B 24 100101
[20]	Hu Q, Zhang Y, Xu X, Xing C, Chen C and Chen X 2015 Acta Phys. Sin. 64 190101 (in Chinese)
[21]	Kitsak M, Gallos L K, Havlin S, Liljeros F, Muchnik L, Stanley H E and Makse H A 2010 Nat. Phys. 6 888
[22]	Borge-Holthoefer J and Moreno Y 2012 Phys. Rev E 85 026116
[23]	Lu Z, Wen Y, Zhang W, Zheng Q and Cao G 2015 IEEE T. Mobile Comput. 15 1292
[24]	Lu Z, Wen Y and Cao G 2014 Proceedings of 33rd Annual IEEE International Conference on Computer Communications, 2014, Toronto, Canada, p. 1932
[25]	Li Y, Qian M, Jin D, Hui P and Vasilakos A V 2015 Inform. Sci. 293 383
[26]	Centola D 2010 Science 329 1194
[27]	Zhao J, Wu J and Xu K 2010 Phys. Rev. E 82 016105
[28]	Hu H, Wen Y, Chua T and Li X 2014 IEEE Access Journal 2 652
[29]	Wen S, Haghighi M S, Chen C, Xiang Y, Zhou W and Jia W 2015 IEEE T. Comput. 64 640
[30]	Wen S, Jiang J J, Xiang Y, Yu S, Zhou W L and Jia W J 2014 IEEE T. Parall. Distr. 25 3306
[31]	Jiang C, Chen Y and Liu K J 2014 IEEE T. Signal Proces. 62 4573
[32]	Lambiotte R, Saramaki J and Blondel V D 2009 Phys. Rev. E 79 046107
[33]	Goldenberg J, Libai B and Muller E 2001 Market. Lett. 12 211

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