Bridging Time Series Dynamics and Complex Network Theory with Application to Electrocardiogram Analysis

Since the pioneering studies of small-world and scale-free networks in the late 1990s [1], [2], we have witnessed fruitful advances in our understanding of the complex connection properties and characteristics of the many diverse large-scale distributed interconnected systems, both natural and man-made, with examples ranging from the Internet, the World Wide Web, biological neural networks, protein-to-protein interaction networks, power-grids, wireless communication networks to social, economic and financial networks in human society [3][10]. Extensive efforts have been devoted to characterizing the rich connectivity patterns among the nodes (components) of such complex networks (systems), and in the course of development of research in this area, people have been prompted to address on a fundamental question: How does the fascinating yet complex topological features of a network affect or determine the collective behavior and performance of the networked system? While elegant attempts to address this core issue have been made, for example, from the viewpoints of synchronization [11][16], epidemics [17][22], evolutionary cooperation [23][26], and the control of complex networks [27][30], theoretically or empirically, this widely concerned key question still remains open in the newly emergent field of network science.