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ORIGINAL RESEARCH article

Front. Netw. Physiol.
Sec. Networks of Dynamical Systems
Volume 4 - 2024 | doi: 10.3389/fnetp.2024.1390319
This article is part of the Research Topic Insights in Networks of Dynamical Systems, Vol II View all 3 articles

A statistical analysis method for probability distributions in Erdös-Rényi random networks with preferentially cutting-rewiring operation

Provisionally accepted
Yu Qian Yu Qian 1*Jiahui Cao Jiahui Cao 1*Jing Han Jing Han 1Siyi Zhang Siyi Zhang 1*Wentao Chen Wentao Chen 2*Zhao Lei Zhao Lei 1*Xiaohua Cui Xiaohua Cui 2*Zhigang Zheng Zhigang Zheng 3*
  • 1 Baoji University of Arts and Sciences, Xi'an, China
  • 2 Beijing Normal University, Beijing, Beijing Municipality, China
  • 3 Huaqiao University, Quanzhou, Fujian, China

The final, formatted version of the article will be published soon.

    Recently, the study of the specific physiological processes from the perspective of network physiology becomes a hot issue, in which modelling the global information integration among the separated functionalized modules in structural and functional brain networks is a central problem. In this paper, the preferentially cutting-rewiring operation (PCRO) is introduced to approximatively describe the above physiological process, which consists of the cutting procedure and the rewiring procedure with specific preferential constraints. By applying the PCRO on the classical Erdös-Rényi random network (ERRN), three types of isolated nodes are generated, based on which the common leaves (CLs) are formed between the two hubs. This makes the initially homogeneous ERRN experience drastic changes and become heterogeneous. Importantly, a statistical analysis method is proposed to theoretically analyze the statistical properties of the ERRN with PCRO. Specifically, the probability distributions of these three types of isolated nodes are derived, based on which the probability distribution of CL can be obtained easily. Furthermore, the validity and the universality of our statistical analysis method have been both confirmed in numerical experiments. Our contributions may shed lights on a new perspective in the interdisciplinary field of complexity science and biological science and would be of great and general interests for network physiology.

    Keywords: Network physiology, Biological science, brain networks, complex systems, network models PACS numbers: 89.75.Kd, 05.65.+b, 89.75.Fb

    Received: 23 Feb 2024; Accepted: 27 Sep 2024.

    Copyright: © 2024 Qian, Cao, Han, Zhang, Chen, Lei, Cui and Zheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yu Qian, Baoji University of Arts and Sciences, Xi'an, China
    Jiahui Cao, Baoji University of Arts and Sciences, Xi'an, China
    Siyi Zhang, Baoji University of Arts and Sciences, Xi'an, China
    Wentao Chen, Beijing Normal University, Beijing, 100875, Beijing Municipality, China
    Zhao Lei, Baoji University of Arts and Sciences, Xi'an, China
    Xiaohua Cui, Beijing Normal University, Beijing, 100875, Beijing Municipality, China
    Zhigang Zheng, Huaqiao University, Quanzhou, 362021, Fujian, China

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