Effective Trace Acquirement during Product Information Diffusion and Application
- 1 Computer Center, Hebei University of Economics and Business, Shijiazhuang, China
- 2 Computer Center, Hebei University of Economics and Business, Shijiazhuang, China
- 3 Computer Center, Hebei University of Economics and Business, Shijiazhuang, China
- 4 Computer Center, Hebei University of Economics and Business, Shijiazhuang, China
- 5 Computer Center, Hebei University of Economics and Business, Shijiazhuang, China
Abstract
Information dissemination has become part of people’s daily communication and there is great interest for both academic and industrial communities. Most previous studies have focused on the strategy and mechanisms. The methods controlling the process of information diffusion have rarely been studied. Thus, previous studies have failed to effectively mine the value of product information diffusion on social networks. In this study, based on the information diffusion product in consumer self-organized social networks, the control of the product information diffusion process was explored. The node identification principle of the QR code sender designed in this study and the linked list that associated information with specific nodes allowed the acquisition of effective traces in long-chain transmission from the information source to the value nodes, and solved user information disclosure during the transmission process. This method was applied to the tracing system of defective vehicles, achieving accurate recall of defective vehicles.
- Cheng, J.J., Liu, Y., Shen, B., et al. (2013) An Epidemic Model of Rumor Diffusion in Online Social Networks. The European Physical Journal B, 86, 29. https://doi.org/10.1140/epjb/e2012-30483-5
- Dybiec, B. (2009) SIR Model of Epidemic Spread with Accumulated Exposure. The European Physical Journal B, 67, 377-383. https://doi.org/10.1140/epjb/e2008-00435-y
- Daley, D.J. and Kendal, D.G. (1964) Epidemic and Rumors. Nature, 204, 1118. https://doi.org/10.1038/2041118a0
- Daley, D.J. and Kendal, D.G. (1965) Stochastic Rumors. IMA Journal of Applied Mathematics, 1, 42-55. https://doi.org/10.1093/imamat/1.1.42
- Daley, D.J. and Gani (2000) Epidemic Modelling. Cambridge University Press, Cambridge.
- Tian, R.Y., Zhang, X.F. and Liu, Y.J. (2015) SSIC Model: A Multi-Layer Model for Intervention of Online Rumors Spreading. Physica A, 427, 181-191. https://doi.org/10.1016/j.physa.2015.02.008
- Denning, R.J. (1985) The Science of Computing: Super Networks. American Scientist, 73, 225-227.
- Xia, L.L., Jiang, G.P., Song, B. and Song, Y.R. (2015) Rumor Spreading Model Considering Hesitating Mechanism in Complex Social Networks. Physica A, 437, 295-303. https://doi.org/10.1016/j.physa.2015.05.113
- Wang, Y.Q., Yang, X.Y., Han, Y.L. and Wang, X.A. (2013) Rumor Spreading Model with Trust Mechanism in Complex Social Networks. Communications in Theoretical Physics, 59, 510-516. https://doi.org/10.1016/j.physa.2015.05.113
- Qian, Z., Tang, S.T., Zhang, X. and Zheng, Z.M. (2015) The Independent Spreaders Involved SIR Rumor Model in Complex Networks. Physica A, 429, 95-102. https://doi.org/10.1016/j.physa.2015.02.022
- Ma, J., Li, D.D. and Tian, Z.H. (2016) Rumor Spreading in Online Social Networks by Considering the Bipolar Social Reinforcement. Physica A, 447, 108-115. https://doi.org/10.1016/j.physa.2015.12.005
- Huo, L.A., Jiang, J.H., Gong, S.X. and He, B. (2016) Dynamical Behavior of Rumor Transmission Model with Holling-Type 2 Functional Response in Emergency Event. Physica A, 450, 228-240. https://doi.org/10.1016/j.physa.2015.12.143
- Giorno, V. and Spina, S. (2016) Rumor Spreading Models with Random Denials. Physica A, 461, 569-576. https://doi.org/10.1016/j.physa.2016.06.070
- Huo, L.A., Wang, L., Song, N.X., et al. (2017) Rumor Spreading Model Considering the Activity of Spreaders in the Homogeneous Network. Physica A, 468, 855-865. https://doi.org/10.1016/j.physa.2016.11.039