Node importance identification for uncertain networks

This paper proposes the concepts of uncertain strength, uncertain degree, and uncertain clustering coefficient under uncertain networks, along with the proposes uncertain k -shell decomposition algorithm and uncertain h -index. Susceptible-Infected-Recovered (SIR) epidemic model is used to simulate the propagation process under uncertain networks. When compared to with the propagation process under uncertain k -shell decomposition algorithm and uncertain h -index, it is discovered that there are few differences among these methods. In order to find a better node importance identification algorithm, this paper combines these methods using the information entropy to form a new node importance identification method, which is called uncertain entropy method. SIR epidemic model is used to illustrate the difference among these methods.