Complex System Reliability Lab
Complex Network
Daqing Li / 李大庆
BUAA Researcher, Ph.D.supervisor
Office address: room 406
Institute of international studies
37 Xueyuan Road
Haidian District, Beijing
100191 Email: Tel. + 86 010 8233 8504
Complex system reliability
Transportation network, information network, social network, financial network, and even brain network, as the "arteries" upporting the normal operation of the system, all have a typical network structure and are mainly used to transport all kinds of traffic (material, energy, information). These network systems are frequently affected by internal failures or external attacks. Traffic paralysis, rumor spreading, credit collapse, etc., are the main failure or threat modes of various networks (collectively referred to as network failures). These critical infrastructure networks have the characteristics that the general systems do not have: they contain a large number of elements, multi-level coupling, nonlinear dynamic evolution, fault emergence, etc. With the traditional reliability method to analysis and calculate the reliability of the complex network or vulnerability, is likely to encounter failure can't locate or "index explosion", lead to actual reliability management in large and unrewarding: according to statistics, the frequency of the blackout for 20 years in the United States have not reduce, even power frequency during peak hours increased (p. ines et al., Energy Policy, 2009). If these networks are egarded as mechanization and Informa ionization of the "forest", the risk or failure is a threat to the forest security of the "fire". In most networks, these "fires" tend to travel along implicit functional coupling trajectories. Understanding the behavior of fire propagation in forest fires can help us establish effective mitigation strategies, which can guide the isolation and elimination of fires as soon as they are detected. Although in practice, the failure propagation of various complex systems often causes much greater losses than forest fires, we still lack a deep understanding of the failure patterns of complex systems.