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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- 1 Baoji University of Arts and Sciences, Xi'an, China
- 2 Beijing Normal University, Beijing, Beijing Municipality, China
- 3 Huaqiao University, Quanzhou, Fujian, China
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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