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Identifying Vital Nodes in Hypernetworks Based on Improved PageRank Algorithm and Information Entropy

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Proceedings of International Conference on Image, Vision and Intelligent Systems 2023 (ICIVIS 2023) (ICIVIS 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1163))

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Abstract

Hypernetworks can reflect the multiple connections of entities in the real-world from multiple dimensions. Identifying the vital nodes in hypernetworks is beneficial to analyze the topology and network functions of the hypernetwork. Traditional methods of vital node identification within a hypernetwork struggles to reach an optimum between time complexity and the precision of identification. As a result, this paper proposes a global and local centrality based on the improved PageRank algorithm and information entropy, which merges the local properties of nodes with their global properties to balance accuracy and time complexity. It is then compared with other methods on real hypernetworks in four different domains by means of monotonicity as well as SI propagation model evaluation criteria. To compare the ranking differentiation and accuracy with other centrality algorithms, we perform numerical simulation experiments on four real networks using the SI model. The experimental findings demonstrate that the proposed method not only yields more precise ranking outcomes but also significantly diminishes the occurrence of identical ranking nodes.

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Acknowledgements

This work was supported by the Major Achievements Transformation Project from Qinghai Province(Nos. 2020-SF-139), the State Key Laboratory of Tibetan Intelligent Information Pro-cessing and Application, the Key Laboratory of Tibetan Intelligent Information Processing and Machine Translation of Qinghai P.R.C and the Key Laboratory of Tibetan Information Processing of Ministry of Education P.R.C.

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Authors and Affiliations

  1. College of Computer, Qinghai Normal University, Xining, 810008, China

    Junjie Chen, Pengyue Li, Haiping Ding, Faxu Li & Defang Wang

  2. School of Mathematics and Statistics, Qinghai Normal University, Xining, 810008, China

    Liang Wei

Authors
  1. Junjie Chen
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  2. Liang Wei
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  3. Pengyue Li
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  4. Haiping Ding
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  5. Faxu Li
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  6. Defang Wang
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Corresponding author

Correspondence to Faxu Li .

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Editors and Affiliations

  1. National University of Defense Technology, Changsha, Hunan, China

    Peng You

  2. Hebei University, Baoding, Hebei, China

    Shuaiqi Liu

  3. Hebei University, Baoding, Hebei, China

    Jun Wang

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Chen, J., Wei, L., Li, P., Ding, H., Li, F., Wang, D. (2024). Identifying Vital Nodes in Hypernetworks Based on Improved PageRank Algorithm and Information Entropy. In: You, P., Liu, S., Wang, J. (eds) Proceedings of International Conference on Image, Vision and Intelligent Systems 2023 (ICIVIS 2023). ICIVIS 2023. Lecture Notes in Electrical Engineering, vol 1163. Springer, Singapore. https://doi.org/10.1007/978-981-97-0855-0_63

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  • DOI: https://doi.org/10.1007/978-981-97-0855-0_63

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0854-3

  • Online ISBN: 978-981-97-0855-0

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