Abstract
In the process of individuals acquiring and sharing knowledge in online social networks, the difference of knowledge internalization ability, the lack of trust and incentive mechanism hinder the effective dissemination and prediction of knowledge. Therefore, it is significant to find effective ways to predict and promote knowledge dissemination in online social networks. In this paper, we establish a novel dynamics model, which considers the complex psychological cognition and behavior of individuals, and adds two new states to describe the dynamic process of knowledge dissemination more accurately compared with the classical infectious disease model. Besides, we investigate the trend of knowledge dissemination and the stability of the proposed model. Our theoretical analysis shows the proposed model can effectively judge and predict the trend of knowledge dissemination through a threshold, and simulation experiments verify the proposed knowledge dissemination dynamics model is reasonable, and it can effectively promote knowledge dissemination.
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References
Yang, C., Fu, L., Gan, X.: Evolving knowledge graph-based knowledge diffusion model. In: 2021 IEEE Wieless Communications and Networking Conference. IEEE, Nanjing, China (2021)
Wang, Y., Cai, Z., Zhan, Z., et al.: An optimization and auction-based incentive mechanism to maximize social welfare for mobile crowdsourcing. IEEE Trans. Comput. Soc. Syst. 6(3), 414–429 (2019)
Banez, R., Gao, H., Li, L., et al.: Modeling and Analysis of opinion dynamics in social networks using mutiple-population mean field games. IEEE Trans. Signal Inf. Process. Netw. 8, 301–316 (2022)
Kar, P., et al.: Are fake images bothering you on social network? Let us detect them using recurrent neural network. IEEE Trans. Comput. Soc. Syst. Early access, 1–12 (2022)
Wang, X., Wang, X., Min, G., et al.: An efficient feedback control mechanism for positive/negative information spread in online social networks. IEEE Trans. Cybern. 52(1), 87–100 (2022)
Jagadishwari, V.: Talkative Friend algorithm for inferring ties in social networks. 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, pp. 181–184 (2021)
Qiu, T., Chen, B., Arun, K., et al.: A survey of mobile social networks: applications, social characteristics, and challenges. IEEE Syst. J. 12(4), 414–429 (2018)
Cai, Z., He, Z., Guan, X., et al.: collective data-sanitization for preventing sensitive information inference attacks in social networks. IEEE Trans. Depend. Sec. Comput. 15(4), 577–590 (2018)
Lin, Y., Wang, X., Ma, H., et al.: An efficient approach to sharing edge knowledge in 5G-enabled industrial Internet of Things. IEEE Trans. Industr. Inf. (2022). https://doi.org/10.1109/TII.2022.3170470
Liu, W., et al.: Global dynamics of knowledge global dynamics of knowledge transmission model on scale-free networks. In: 2019 Chinese Control Conference. IEEE, Guangzhou, China (2019)
Wang, H., Wang, J., Small, M.: Knowledge transmission model with differing initial transmission and retransmission process. Phys. A 507, 478–488 (2018)
Liao, S., Yi, S.: Modeling and analysis knowledge transmission process in complex networks by considering internalization mechanism. Chaos Soliton Fract. 143, 110593 (2021)
Wang, Y., Cao, J.: Global dynamics of a network epidemic model for waterborne diseases spread. Appl. Math. Comput. 237, 474–488 (2014)
Li, M., Shuai, Z.: Global-stability problem for coupled systems of differential equations on networks. J. Diff. Equ. 248(1), 1–20 (2010)
Wang, H., Wang, J., Ding, L., et al.: Knowledge transmission model with consideration of self-learning mechanism in complex networks. Appl. Math. Comput. 304, 83–92 (2017)
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Hao, Y., Wang, X., Lin, Y., Zhang, C. (2022). Dynamics Modeling of Knowledge Dissemination Process in Online Social Networks. In: Ma, H., Wang, X., Cheng, L., Cui, L., Liu, L., Zeng, A. (eds) Wireless Sensor Networks. CWSN 2022. Communications in Computer and Information Science, vol 1715. Springer, Singapore. https://doi.org/10.1007/978-981-19-8350-4_12
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DOI: https://doi.org/10.1007/978-981-19-8350-4_12
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