Measuring microscopic evolution processes of complex networks based on empirical data

[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.

[发布机构] 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

[效力级别] 数学

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