Node importance evaluation based on neighborhood structure hole and improved TOPSIS

摘要

It is of great significance to identify the important nodes accurately and rapidly for preventing accidents in the network. This paper improves the traditional neighborhood structure hole indicators from the perspective of the topology and network characteristics between a node and its neighbors, and the improved indicators are applied to the importance evaluation of the network node. In order to avoid the subjectivity of artificially determining the indicator weights, we use Gini coefficient and Kendall coefficient to calculate the objective weight of evaluation indicators by combining the difference among the indicator values and the conflict among the indicators. Correspondingly, this paper proposes the information efficiency of the evaluation indicators to measure the contribution of traditional indicators and improved indicators. At the same time, the prospect theory is adopted to solve the problem of the traditional node evaluation methods that the influence of decision-makers' experience and knowledge on evaluation results is easily neglected. For the shortages of the traditional TOPSIS method, the relative entropy is utilized to solve the problem that the vertical line nodes in the positive and negative ideal solutions cannot be distinguished effectively in the traditional TOPSIS, and the gray correlation is introduced to measure the curve edge coupling degree to enhance the accuracy of the evaluation result. The evaluation and comparison results of the ARPA network and the standard IEEE 39-bus system validate the effectiveness and superiority of the improved indicators and evaluation methods.