Complex networks are constructed to study correlations between the closing prices for all US stocks that were traded over two periods of time (from July 2005 to August 2007; and from June 2007 to May 2009). The nodes are the stocks, and the connections are determined by cross correlations of the...

Recently, it has been found that the degree distribution of a user network has a significant effect on the traffic carried by a telephone network. In this paper, we investigate the effect of user clustering on the telephone traffic based on computer simulations. Both uniform and scale-free user...

The traffic in telephone networks is analyzed in this paper. Unlike the classical traffic analysis where call blockings are due to the limited channel capacity, we consider here a more realistic cause for call blockings which is due to the way in which users are networked in a real-life human...

Co-occurrence networks of Chinese characters and words, and of English words, are constructed from collections of Chinese and English articles, respectively. Four types of collections are considered, namely, essays, novels, popular science articles, and news reports. Statistical parameters of...

The attractive possibility that financial indices may be chaotic has been the subject of much study. In this paper we address two specific questions: "Masked by stochasticity, do financial data exhibit deterministic nonlinearity?", and "If so, so what?". We examine daily returns from three...

The paper studies the dynamical model of motor cognition of neural networks through the theory of stochastic phase resetting dynamics, presents the interaction, phase coding, and the evolution of the time-varying averaged number density in terms of populations of perceptive neurons,...

This paper addresses the theoretical analysis of synchronization between two complex networks with nonidentical topological structures. By designing effective adaptive controllers, we achieve synchronization between two complex networks. Both the cases of identical and nonidentical network...