Measuring microscopic evolution processes of complex networks based on empirical data

4th International Workshop on Statistical Physics and Mathematics for Complex Systems

物理学;数学

Chi, Liping^1,2

Complexity Science Research Center, College of Physical Science and Technology, Central China Normal University, Wuhan

430079, China^1

Center for Complex Network Research, Department of Physics, Northeastern University, Boston

MA

02115, United States^2

关键词: Autonomous systems;    Degree distributions;    Dynamical process;    Evolution process;    Microscopic mechanisms;    Network topology;    Nodes and links;    Scientific collaboration;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/604/1/012004/pdf
DOI  :  10.1088/1742-6596/604/1/012004

来源: IOP

【 摘 要 】

Aiming at understanding the microscopic mechanism of complex systems in real world, we perform the measurement that characterizes the evolution properties on two empirical data sets. In the Autonomous Systems Internet data, the network size keeps growing although the system suffers a high rate of node deletion (r = 0.4) and link deletion (q = 0.81). However, the average degree keeps almost unchanged during the whole time range. At each time step the external links attached to a new node are about c = 1.1 and the internal links added between existing nodes are approximately m = 8. For the Scientific Collaboration data, it is a cumulated result of all the authors from 1893 up to the considered year. There is no deletion of nodes and links, r = q = 0. The external and internal links at each time step are c = 1.04 and m = 0, correspondingly. The exponents of degree distribution p(k) ∼ k-γof these two empirical datasets γdata are in good agreement with that obtained theoretically γtheory. The results indicate that these evolution quantities may provide an insight into capturing the microscopic dynamical processes that govern the network topology.