Complex web-like structures describe a wide variety of real-world systems of high technological and intellectual importance. Despite enormous benefits to daily life, complex network systems also exhibit disadvantages, and one of the most challenging issues is the risk and safety of complex network systems. In a network system, the impact of a local fault/disturbance/hazard can easily spread out to the whole system due to domino effect, cascading effect, and/or ripple effect and eventually evolves into a large-scale disaster. The past decade has witnessed too many examples, to name some, the 2003 Northeast blackout in USA, the 2003 SARS plague, the 2004 breakout of computer worm Bagle, the 2008 global financial crisis, and the 2010 European aviation network closure due to the Eyjafjallajokull Volcano eruption in Iceland. Besides the nature of hazard/fault/disturbance and the topology of system, many other factors such as heterogeneity in components, interaction functions and rules, amplifying effect, self-healing capability, and multiple feedback loops altogether play a crucial role in determining the system performance against hazard/fault/disturbance. The complexity roots in the combination of all these factors, resulting in nonlinear, dynamical, self-adaptive, and self-organizing behavior of system, which is far beyond the capability of existing methodologies. To study the risk and safety of complex network systems, new interdisciplinary theories, models, and methods are urgently demanded and particularly needed to fertilize new thinking of globally networked risks and complex systems science. This special issue aims to bring together both relevant theoretical works and case studies, in order to inspire, promote, and enrich the research on the risk and safety of complex network systems.

Potential topics include, but are not limited to:

• Transportation systems
• Communication networks and information systems
• Cyber world security
• Energy systems (such as smart grids or gas pipe networks)
• Financial systems
• Social networks and perception
• Biological and medical systems
• Disaster and risk reduction and mitigation
• Self-organizing and self-healing networks
• Reliability and safety theories
• Mathematical theories of complex networks (such as synchronization and control of complex networks)
• New theories, models, and methods to assess risk and improve safety