Measurements and Evolution of Complex Networks with Propagation Dynamics

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With the development of technology, we live in a world which is surrounded by complex networks, e.g., the power grid, transportation network, Internet, neural networks, social networks. Understanding the structure and dynamics of these extremely complex interactive networks has become one of the key research topics and challenges of life science in the 21st century. For example, the coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, hygienic habits and the communication methods. In order to control the virus spread, it is very necessary to analyze the network structures and epidemic dynamics, e.g., the importance of nodes in the networks, the influence of network structure measurements on propagation, the interaction between propagation dynamics and the structure measurements, and the construction of epidemiological models that can capture the effects of these changes in mobility on the spread of virus. Meanwhile, the results of these studies can also be used as a reference for the study of multiple propagation behaviors in other networks. Complex network theory is to study the commonness of these seemingly different complex networks and the universal methods to deal with them. In 1998 and 1999, the finding of small world effects and scale-free property has attracted a great deal of attention of network structures and dynamics, which raises the science awareness for the real world. After the discovery of small world effects and scale-free property of networks, researchers gradually realize and study the complexity of networks. More network structure metrics are proposed, and more network characteristics are found with the development of complex network research. […] In the thesis, the influence of complex network structure measurements on the propagation processes and the dynamic relationship between network structures and the propagation processes are studied. Firstly, the influence of network structure measurement on the propagation process is studied and applied to the process of node influence identification, cascading failure and virus propagation. Based on the degree value of the nodes, a method to quickly identify the influence of the nodes, as well as a cascaded failure model considering the local real-time information priority redistribution strategy, is proposed, and a novel metric is proposed to measure the robustness in regard to virus attacks in social networks. Following on from this, the cooperative evolution of network structure and propagation process is studied, and the reliability of adaptive weighted networks is analyzed and discussed.
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