The capacity of maximum end-to-end traffic flow the network is able to handle without overloading is an important index for network performance in real communication systems. In this paper, we estimate the variations of network capacity under different routing strategies for three different topologies. Simulation results reveal that the capacity depends on the underlying network structure and the capacity increases as the network becomes more homogeneous. It is also observed that the network capacity is greatly enhanced when the new traffic awareness routing strategy is adopted in each network structure.

Network methods have had profound influence in many domains and disciplines in the past decade. Community structure is a very important property of complex networks, but the accurate definition of a community remains an open problem. Here we defined community based on three properties and then propose a simple and novel framework to detect communities based on network topology. We analyzed 16 different types of networks and compared our partitions with Infomap, LPA, Fastgreedy and Walktrap, which are popular algorithms for community detection. Most of the partitions generated using our approach compare favorably to those generated by these other algorithms. Furthermore, we define overlapping nodes that combine community structure with shortest paths. We also analyzed the E. Coli. transcriptional regulatory network in detail and identified modules with strong functional coherence.

In this article, a distributed active disturbance rejection formation tracking control strategy is developed for a quadrotor unmanned aerial vehicle (UAV) swarm system with switching communication topologies. The proposed control strategy consists of two parts: 1) attitude-loop control and 2) position-loop control. First, a distributed cascade active disturbance rejection control (CADRC) method is designed for the attitude subsystem. With this attitude control method, the stability of the attitude subsystem can be achieved even in the presence of unknown time-varying disturbances. Second, a distributed formation tracking control method is designed for the position subsystem. This position control method ensures that the quadrotor UAV swarm maintains dynamic formation flying and accurately tracks a predetermined trajectory. Through stability analysis, it can be proved that the proposed control strategy can ensure the stability of the whole swarm system. Finally, the proposed control strategy is applied to a quadrotor UAV swarm system to verify its effectiveness and the ability to suppress the influence of unknown time-varying disturbances.

Identifying influential spreaders in complex networks has a significant impact on understanding and control of spreading process in networks. In this paper, we introduce a new centrality index to identify influential spreaders in a network based on the community structure of the network. The community-based centrality (CbC) considers both the number and sizes of communities that are directly linked by a node. We discuss correlations between CbC and other classical centrality indices. Based on simulations of the single source of infection with the Susceptible-Infected-Recovered (SIR) model, we find that CbC can help to identify some critical influential nodes that other indices cannot find. We also investigate the stability of CbC.