Information propagation in online social network based on human dynamics. (English) Zbl 1272.91111

Yan, Qiang; Wu, Lianren; Liu, Chao; Li, Xiye

Abstr. Appl. Anal. 2013, Article ID 953406, 6 p. (2013).

Summary: We investigate the impact of human dynamics on the information propagation in online social networks. First, statistical properties of the human behavior are studied using the data from the “Sina Microblog”, which is one of the most popular online social networks in China. We find that human activity patterns are heterogeneous and bursty and are often described by a power-law interevent time distribution \(P(\tau) \sim \tau^{-\alpha}\). Second, we proposed an extended Susceptible-Infected (SI) propagation model to incorporate bursty and limited attention. We unveil how bursty human behavior and limited attention affect the information propagation in online social networks. The result in this paper can be useful for optimizing or controlling information propagation in online social networks.

MSC:

91D30	Social networks; opinion dynamics
68M11	Internet topics

References:

[1]	Yan, Q.; Wu, L.; Yi, L., Research on the human dynamics in mobile communities based on social identity, Discrete Dynamics in Nature and Society, 2012 (2012)
[2]	Xiao, Y. P.; Wang, B.; Liu, Y. B.; Yan, Z. X.; Chen, X.; Wu, B.; Xu, G. X.; Liu, Y. N., Analyzing, modeling, and simulation for human dynamics in social network, Abstract and Applied Analysis, 2012 (2012)
[3]	Xiao, Y. P.; Wang, B.; Wu, B.; Yan, Z.; Jia, S.; Liu, Y., A hybrid human dynamics model on analyzing hotspots in social networks, Discrete Dynamics in Nature and Society, 2012 (2012) · Zbl 1253.91159
[4]	Yan, Q.; Wu, L., Impact of bursty human activity patterns on the popularity of online content, Discrete Dynamics in Nature and Society, 2012 (2012)
[5]	Barabási, A. L., The origin of bursts and heavy tails in human dynamics, Nature, 435, 7039, 207-211 (2005)
[6]	Jo, H. H.; Karsai, M.; Kertesz, J.; Kaski, K., Circadian pattern and burstiness in mobile phone communication, New Journal of Physics, 14 (2012)
[7]	Vazquez, A.; Oliveira, J. G.; Dezso, Z.; Goh, K. I.; Kondor, I.; Barabasi, A. L., Modeling bursts and heavy tails in human dynamics, Physical Review E, 73 (2006)
[8]	Oliveira, J. G.; Barabási, A. L., Human dynamics: Darwin and Einstein correspondence patterns, Nature, 437, 7063, 1251 (2005)
[9]	Zhao, Z. D.; Xia, H.; Shang, M. S.; Zhou, T., Empirical analysis on the human dynamics of a large-scale short message communication system, Chinese Physics Letters, 28, 6 (2011)
[10]	Hong, W.; Han, X. P.; Zhou, T.; Wang, B. H., Heavy-tailed statistics in short-message communication, Chinese Physics Letters, 26 (2009)
[11]	Wu, Y.; Zhou, C.; Xiao, J.; Kurths, J.; Schellnhuber, H. J., Evidence for a bimodal distribution in human communication, Proceedings of the National Academy of Sciences of the United States of America, 107, 44, 18803-18808 (1880)
[12]	Wu, Y.; Ye, Q.; Li, L.; Xiao, J., Power-law properties of human view and reply behavior in online society, Mathematical Problems in Engineering, 2012 (2012) · Zbl 1264.91098
[13]	Min, B.; Goh, K. I.; Vazquez, A., Spreading dynamics following bursty human activity patterns, Physical Review E, 83, 3 (2011)
[14]	Vazquez, A.; Racz, B.; Lukacs, A.; Barabasi, A. L., Impact of non-Poissonian activity patterns on spreading processes, Physical Review Letters, 98 (2007)
[15]	Weng, L.; Flammini, A.; Vespignani, A.; Menczer, F., Competition among memes in a world with limited attention, Scientific Reports, 2, article 335 (2012)
[16]	Yan, Q.; Yi, L. L.; Wu, L. R., Human dynamic model co-driven by interest and social identity in the MicroBlog community, Physica A, 391, 1540-1545 (2012)
[17]	Dong, H. L.; Wang, Z. D.; Gao, H. J., Distributed filtering for a class of time-varying systems over sensor networks with quantization errors and successive packet dropouts, IEEE Transactions on Signal Processing, 60, 6, 3164-3173 (2012) · Zbl 1391.93232
[18]	Dong, H. L.; Wang, Z. D.; Ho, D. W. C.; Gao, H. J., Distributed H-infinity state estimation with stochastic parameters and nonlinearities through sensor networks: the finite-horizon case, Automatica, 48, 8, 1575-1585 (2012) · Zbl 1267.93167
[19]	Vazquez, A., Polynomial growth in branching processes with diverging reproductive number, Physical Review Letters, 96, 3 (2006)
[20]	Harris, T. E., The Theory of Branching Processes (2002), Berlin, Germany: Springer, Berlin, Germany · Zbl 1037.60001
[21]	Feller, W., An Introduction to Probability Theory and Its Applications (1967), New York, NY, USA: Wiley, New York, NY, USA · Zbl 0158.34902