Collapse Of The Cascade Of Complex Networks And The Spread Of The Virus Research

It is now universally acknowledged that complex network is a novel and efficient tool for characterizing and studying various complex structures in both nature and society. Recently people have witnessed great advances in this subject. Complex networks created by several typical models (such as scale-free network model) provide more accurate description for the real complex systems. Naturally, the functions and dynamical processes on complex systems have become a new hot subject in the field of complex networks. Specially, some dangerous events on real life networks, including big blackout accidents on power plant network, information congestion on the Internet and virus prevalence in computer network, have aroused people's special attention. It is helpful to prevent or alleviate the danger brought by these dangerous events if we try to do some research on them. We have done some research on cascading breakdown and virus prevalence in complex network in this thesis. The main contents and originalities in this thesis are listed as follows:1. Cascading breakdown on weighted complex networksIn real life networks (such as power plant network), each link has its own limited capacity for packet flows. A link's breakdown is the result of load on it exceeding its capacity for packet flows, which may lead to the accident of cascading breakdown. Therefore, after constructing a model for cascading breakdown on weighted complex network, we studied the different phenomena of cascading breakdown on different weighted complex networks. We found that it is efficient to alleviate the danger brought by cascading breakdown using the strategy of weight preferential strengthening.2. Virus prevalence in scale-free networks with community structureCommunity structure, which exists broadly in real life networks, can influence virus prevalence in complex networks. Using MATLAB software, we have compared the different behaviors of virus prevalence between scale-free network with and without community structure. We found the speed at which virus prevalence on a network decreases as the network presents a stronger community structure.