基于加权超网络模型的知识网络鲁棒性分析及应用

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

对知识网络的鲁棒性分析方法进行了研究.与一般的复杂网络相比,知识网络涉及两种不同类型的节点:知识和知识主体.在进行鲁棒性研究时,必须对二者进行综合考虑.为此提出基于加权超网络模型的知识网络鲁棒性分析方法,该模型可根据组织中知识与知识主体之间的映射关系将二者集成在一起.在此基础上,提出了一种关联节点删除的方法来研究知识网络的鲁棒性,并提出了度量知识网络鲁棒性的专有知识率、专有知识加权比率、知识网络抗毁性、核心领域知识网络抗毁性等指标及其分析方法,解决了知识网络的鲁棒性分析及度量的问题,并可应用于组织知识资源的安全性评估、发现易流失知识以及评价组织成员的知识重要性等方面.

The method to analyze the robustness of knowledge network is discussed in the paper. As different to general complex network, there are two different types of nodes in knowledge network： knowledge and knowledge owners such as persons, enterprises and so on. To analyze the robustness of 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. Based on the supernetwork model, a combined node removal method is proposed. To measure the robustness of knowledge network, some signals such as the unique knowledge proportion, the weighted proportion of unique knowledge, the resilience of knowledge network, the resilience of core field knowledge network, are proposed and analyzed. The method proposed in the paper can successfully analyze and measure the robustness of knowledge network, and can also be applied to assess the security of knowledge resource in an organization, to discover the knowledge points that are easy to be lost, and to evaluate the importance of each member in the organizational knowledge.