This paper considers the consensus problem of dynamical multiple agents that communicate via a directed moving neighbourhood random network. Each agent performs random walk on a weighted directed network. Agents inter...This paper considers the consensus problem of dynamical multiple agents that communicate via a directed moving neighbourhood random network. Each agent performs random walk on a weighted directed network. Agents interact with each other through random unidirectional information flow when they coincide in the underlying network at a given instant. For such a framework, we present sufficient conditions for almost sure asymptotic consensus. Numerical examples are taken to show the effectiveness of the obtained results.展开更多
In network theory,a complex network represents a system whose evolving structure and dynamic behavior contribute to its robustness.The natural connectivity is recently proposed as a spectral measure to characterize th...In network theory,a complex network represents a system whose evolving structure and dynamic behavior contribute to its robustness.The natural connectivity is recently proposed as a spectral measure to characterize the robustness of complex networks.We decompose the natural connectivity of a network as local natural connectivity of its connected components and quantify their contributions to the network robustness.In addition,we compare the natural connectivity of a network with that of an induced subgraph of it based on interlacing theorems.As an application,we derive an inequality for eigenvalues of Erdös-Rényi random graphs.展开更多
文摘This paper considers the consensus problem of dynamical multiple agents that communicate via a directed moving neighbourhood random network. Each agent performs random walk on a weighted directed network. Agents interact with each other through random unidirectional information flow when they coincide in the underlying network at a given instant. For such a framework, we present sufficient conditions for almost sure asymptotic consensus. Numerical examples are taken to show the effectiveness of the obtained results.
文摘In network theory,a complex network represents a system whose evolving structure and dynamic behavior contribute to its robustness.The natural connectivity is recently proposed as a spectral measure to characterize the robustness of complex networks.We decompose the natural connectivity of a network as local natural connectivity of its connected components and quantify their contributions to the network robustness.In addition,we compare the natural connectivity of a network with that of an induced subgraph of it based on interlacing theorems.As an application,we derive an inequality for eigenvalues of Erdös-Rényi random graphs.
