Analysis on real networks by classical multidimensional scaling

摘要

In the real world, we have a wide variety of complex networks, such as Internet, neural networks, human relationships and so on. To understand characteristics and structure of these complex networks, a new framework of combining the complex network theory and nonlinear time series analysis has been proposed. One of the frameworks uses the classical multidimensional scaling to transform complex networks to time series. In this report, by using this transforming method, we investigated the distribution of coordinate values of the time series transformed from the networks. We compared the distribution of coordinate values of real networks with that of network models such as the Watts-Strogatz model and the Barabasi-Albert model.