All of real-world complex systems are not static but dynamic.Researching on the dynamic evolutionary characteristics of these systems can help us to construct some effective evolution models and further to create some corresponding artificial systems. Based on these artificial systems, we can conduct a large amount of computational experiments. Furthermore, the interactions between artificial systems and real systems can evaluate these experimental results. This work can potentially facilitate us control and manage those real-world complex systems in a wide variety of fields. However, the achievement of these goals should be done firstly by selecting many significant but specific real-world systems, and meaningful understanding regarding the formation and the evolutionary mechanisms of these systems. By comparing the differences of these systems and concluding their common places, we can extract more general theories and methodologies and then apply those into other similar systems. As such,this dissertation selects three emerging social-technical systems including infectious diseases, open source software and social tagging systems respectively. By extensively collecting related data and constructing a large number of corresponding networks based on complex network theory, some significant dynamic evolutionary characteristics of these three systems are found. The main work of this dissertation as follows: 1) More frequent emerging infectious diseases recently have prompted a world-wide response and have had a dramatic impact on many countries. From a research perspective, significant efforts from both public health and related fields including but not limited to various subareas of informatics and computer-based modeling, have been devoted to studying the evolution and transmission patterns of those infectious diseases for future prevention and treatment purpose. This dissertation selects Beijing SARS outbreak in 2003 as a study case and analyzing its dynamic evolutionary characteristics by constructing several different kinds of networks from different perspectives based on real-world SARS infection data. In the original data, some bridge links may be missing.These links could provide more potential useful information to analyze the transmission patterns of the epidemic. Therefore, this dissertation continue to investigates some statistical prediction models to address this problem. After these predicted bridge links adding into the SARS trans...
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