Method to Analyze Robustness of Knowledge Network based on Weighted Supernetwork Model and Its Application

Abstract

Abstract The method of analyzing the robustness of a knowledge network is discussed in this article. As against a general complex network, there are two different types of nodes in a knowledge network: knowledge and knowledge owners such as persons, enterprises, and so on. To analyze the robustness of a knowledge network, both types of nodes should be taken into account. To meet the requirement, a method based on the weighted supernetwork model is proposed in which the two types of nodes are integrated together according to the relation mappings between them. On the basis of the supernetwork model, a combined node removal method is proposed. To measure the robustness of a knowledge network, some signals, such as, the unique knowledge proportion, the weighted proportion of unique knowledge, the resilience of the knowledge network, and the resilience of the core field knowledge network, are proposed and analyzed. The method proposed in this article can successfully analyze and measure the robustness of a knowledge network, and can also be applied to assess the security of a knowledge resource in an organization, to discover the knowledge points that are easily lost, and to evaluate the importance of each member in the organizational knowledge.