Network Risk Propagation Model Based On Boundary-Center-Point-Search Algorithm

Abstract

Because the power CPS network with community structure has the unique structural characteristics of internal nodes and boundary nodes, the boundary nodes are more likely to cause cascading failures when attacked, which plays an extremely important role in network risk fault propagation. In this paper, the Boundary node is regarded as an important immune object, and these key node targets are quickly identified to take immune measures. A Boundary center-point-search (BCS) algorithm is proposed. According to the disadvantage of the original risk propagation model which ignores the fact that normal nodes can get immunity through artificial means, a risk propagation model is proposed. Compared with other local immune algorithms, the proposed BCS immune algorithm not only can accurately identify the boundary center points between communities, but also has a faster computational speed, which can provide reference for risk propagation control and prevention in large-scale complex networks with incomplete structural information.