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Controlling a complex network is of great importance in many applications. The network can be controlled by inputting external control signals through some selected nodes, which are called input nodes. Previous works found that the majority of the nodes in dense networks are either the input nodes or not, which leads to the bimodality in controlling the complex networks. Due to the physical or economic constraints of many real control scenarios, altering the control mode of a network may be critical to many applications. Here we develop a graph-based algorithm to alter the control mode of a network. The main idea is to change the control connectivity of nodes by removing carefully selected edges. We rigorously prove the correctness of our algorithm and evaluate its performance on both synthetic and real networks. The experimental results show that the control mode of a network can be easily changed by removing few selected edges. Our methods provide the ability to design the desired control mode for different control scenarios, which may be useful in many applications.
Cooperative Intelligent Transportation Systems (C-ITS) will change the modes of road safety and traffic management, especially at intersections without traffic lights, namely unsignalized intersections. Existing researches focus on vehicle control wi
We study the robustness of complex networks subject to edge removal. Several network models and removing strategies are simulated. Rather than the existence of the giant component, we use total connectedness as the criterion of breakdown. The network
Controlling a complex network towards a desired state is of great importance in many applications. A network can be controlled by inputting suitable external signals into some selected nodes, which are called driver nodes. Previous works found there
Stochastic processes can model many emerging phenomena on networks, like the spread of computer viruses, rumors, or infectious diseases. Understanding the dynamics of such stochastic spreading processes is therefore of fundamental interest. In this w
Stochastic models in which agents interact with their neighborhood according to a network topology are a powerful modeling framework to study the emergence of complex dynamic patterns in real-world systems. Stochastic simulations are often the prefer