Abstract
University library is the auxiliary institution of the university, which mainly provides various services for teachers, students and readers. However, with the advent of the information age, the establishment of modern digital libraries makes library management more difficult. Human resource management of university library is an important measure to improve the comprehensive quality of librarians, an important step to promote the development of libraries, and also of great significance to the development of universities. Therefore, it is an issue that needs to be paid attention to in the current university management process. The purpose of this paper is to study the optimization of university library personnel management based on the complex network theory. In this paper, the definition of complex network is briefly described, and then the degree of complex network is analyzed. The experiment part takes the library graduate student reader’s borrowing information construction network as the research object, carries on the library borrowing network research, emphatically studies the library borrowing network complex network characteristic, carries on the thorough analysis to the library borrowing network community division. The experimental results show that the module degree Q value of the first community partition is the largest, which is 0.3029.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lin, Z., Wen, F., Xue, Y.: A restorative self-healing algorithm for transmission systems based on complex network theory. IEEE Trans. Smart Grid 7(4), 2154–2162 (2017)
Yushu, S., Xisheng, T., Guowei, Z., et al.: Dynamic power flow cascading failure analysis of wind power integration with complex network theory. Energies 11(1), 63 (2017)
Zhang, L., Lu, J., Zhou, J., et al.: Complexities’ day-to-day dynamic evolution analysis and prediction for a Didi taxi trip network based on complex network theory. Mod. Phys. Lett. B 32(9), 1850062 (2018)
Wang, Y.H., Shen, X.R., Yang, S.Q., et al.: Three-dimensional dynamic analysis of observed mesoscale eddy in the South China Sea based on complex network theory. EPL (Europhys. Lett.) 128(6), 60005 (2020)
Strozzi, F., Garagiola, E., Pozzi, R., et al.: Length of stay reduction in the emergency department and its quantification using complex network theory. Int. J. Oper. Res. 36(1), 1 (2019)
Zhou, C., Ding, L., Zhou, Y., et al.: Visibility graph analysis on time series of shield tunneling parameters based on complex network theory. Tunn. Undergr. Space Technol. 89(Jul), 10–24 (2019)
Zekun, W., Xiangxi, W., Minggong, W.U.: Identification of key nodes in aircraft state network based on complex network theory. IEEE Access 7, 60957–60967 (2019)
Li, G.J., Hu, J., Song, Y., et al.: Analysis of the Terrorist Organization Alliance Network based on complex network theory. IEEE Access 7(99), 103854–103862 (2019)
Chong, P., Yin, H.: Analysis on optimization strategies of hazardous materials road transportation network using complex network theory. Complex Syst. Complex. 15(3), 56–65 (2018)
Gao, L., Cao, J.H., Song, T.L., et al.: Evolution model of equipment support system of systems based on complex network theory. Binggong Xuebao/Acta Armamentarii 38(10), 2019–2030 (2017)
Acknowledgements
This work was supported by 203771.
In 2020, chongqing higher education teaching reform project (steering committee) project (203771) under the “major public emergency mechanism of emergency management in university library and services research”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, X. (2021). Personnel Management Optimization of University Library Based on Complex Network Theory. In: MacIntyre, J., Zhao, J., Ma, X. (eds) The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing, vol 1282. Springer, Cham. https://doi.org/10.1007/978-3-030-62743-0_122
Download citation
DOI: https://doi.org/10.1007/978-3-030-62743-0_122
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62742-3
Online ISBN: 978-3-030-62743-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)