Sheng Hong was born in China, in 1981. He received his master degree and doctoral degree in communication and information system from Beihang University, in 2005 and 2009, respectively. He is now a graduate student advisor in the School of Reliability and System Engineering of Beihang University. His recent interests include signal processing, information system modeling, prognostics and heath management. Zheng Zhou was born in China, in 1989. He was a graduate student in Beihang University for master degree. He is now with the systems engineering research institute, CSSC.

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Rumors mislead judgments of people, affect economic development, and the stability of social order. The research on the rule of spreading rumors is significant and meaningful. This paper improves the traditional Barabási–Albert scale‐free network and proposes a network topology model that conforms to the characteristics of sharing social networks based on the complex network theory and the actual characteristics of sharing social networks. In addition, the credulous spider rational taciturn rumor propagation model is proposed by improving the credulous spider rational model, which solves the overspread problem of the traditional rumor propagation model. This paper further studies the influence of anxiety on the spread of rumors, and finds that the anxiety of audience is increasing with the spread degree of rumors.

In modern society, many infrastructures are interdependent owing to functional and logical relations among components in different systems. These networked infrastructures can be modeled as interdependent networks. In the real world, different networks carry different traffic loads whose values are dynamic and stem from the load redistribution in the same network and disturbance from the interdependent network. Interdependency makes interdependent networks so fragile that even a slight initial disturbance may lead to a cascading failure of the entire systems. In this paper, interdependencies among networks are modeled and a failure cascade process is studied considering their effects on failure propagation. Meanwhile, an in-process restoration strategy after the initial failure is investigated. The restoration effects depend strongly on the trigger timing, restoration probability and priority of the restoration actions along with the additional disturbances. Our findings highlight the necessity to decrease the large-scale cascading failure by structuring and managing an interdependent network reasonably.

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Complex networks have been widely studied recent years, but most researches focus on the single, non-interacting networks. With the development of modern systems, many infrastructure networks are coupled together and therefore should be modeled as interdependent networks. For interdependent networks, failure of nodes in one network may lead to failure of dependent nodes in the other networks. This may happen recursively and lead to a failure cascade. In the real world, different networks carry different traffic loads. Overload and load redistribution may lead to more nodes’ failure. Considering the dependency between the interdependent networks and the traffic load, a small fraction of fault nodes may lead to complete fragmentation of a system. Based on the robust analysis of interdependent networks, we propose a costless defense strategy to suppress the failure cascade. Our findings highlight the need to consider the load and coupling preference when designing robust interdependent networks. And it is necessary to take actions in the early stage of the failure cascade to decrease the losses caused by the large-scale breakdown of infrastructure networks.