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Hypernetwork Model Based on Logistic Regression

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Big Data (BigData 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1320))

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Abstract

The evolution of hypernetworks is mostly based on growth and preferential connection. During the construction process, the number of new nodes and hyperedges is increasing infinitely. However, in the network, considering the impact of real resources and environment, the nodes can not grow without the upper limit, and the number of connections can not grow without the upper limit. Under certain conditions, there will be the optimal or maximum growth number. In addition, with the increase of the size of the hypernetwork and the number of nodes, there will be more old nodes waiting to be selected. Based on the problems in the process of constructing the hypernetwork, this paper improves the construction of the hypernetwork, and proposes a hypernetwork model construction method based on logical regression. Firstly, the maximum growth number of nodes is set to limit its growth, and the maximum capacity is set for each hyperedge; Secondly, the connection between nodes is selected by using logical regression instead of preferential connection; Finally, the old nodes are selected by using sub linear growth function instead of constant. Through the simulation experiment, it is found that the hyperdegree distribution of the improved hypernetwork model conforms to the power-law distribution; by changing the network scale, hyperedge capacity, the number of new nodes added and the number of old nodes selected in the process of the construction of the hypernetwork, the change law of the hypernetwork model's hyperdegree distribution is studied.

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Acknowledgment

This work is partially supported by the National Natural Science Foundation of China under Grant No. 11661069, No. 61663041, No. 61763041, the Natural Science Foundation of Qinghai Province of China under Grant No. 2020-GX-112, and the Youth Natural Science Foundation of Qinghai Normal University under Grant No. 2020QZR007, the Chun Hui Project from the Ministry of Education of China under Grant No. Z2016101.

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Authors and Affiliations

  1. College of Computer, Qinghai Normal University, Xining, 810001, Qinghai, China

    Lei Meng, Zhonglin Ye, Haixing Zhao, Yanlin Yang & Fuxiang Ma

  2. Key Laboratory of Tibetan Information Processing, Ministry of Education, Xining, 810008, Qinghai, China

    Lei Meng, Zhonglin Ye, Haixing Zhao & Yanlin Yang

  3. Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province, Xining, 810008, Qinghai, China

    Lei Meng, Zhonglin Ye, Haixing Zhao & Yanlin Yang

Authors
  1. Lei Meng
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  2. Zhonglin Ye
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  3. Haixing Zhao
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  4. Yanlin Yang
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  5. Fuxiang Ma
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Corresponding author

Correspondence to Haixing Zhao .

Editor information

Editors and Affiliations

  1. PLA Academy of Military Sciences, Beijing, China

    Hong Mei

  2. Southwest University, Chongqing, China

    Weiguo Zhang

  3. The University of Edinburgh, Edinburgh, UK

    Wenfei Fan

  4. Southwest University, Chongqing, China

    Zili Zhang

  5. Nanjing University, Nanjing, China

    Yihua Huang

  6. Zhejiang University, Hangzhou, China

    Jiajun Bu

  7. Nanjing University, Nanjing, China

    Yang Gao

  8. Taiyuan University of Technology, Taiyuan, China

    Li Wang

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Meng, L., Ye, Z., Zhao, H., Yang, Y., Ma, F. (2021). Hypernetwork Model Based on Logistic Regression. In: Mei, H., et al. Big Data. BigData 2020. Communications in Computer and Information Science, vol 1320. Springer, Singapore. https://doi.org/10.1007/978-981-16-0705-9_14

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  • DOI: https://doi.org/10.1007/978-981-16-0705-9_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0704-2

  • Online ISBN: 978-981-16-0705-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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