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Personnel Management Optimization of University Library Based on Complex Network Theory

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The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1282))

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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.

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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”.

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Correspondence to Xinyu Wu .

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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

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