Key nodes in complex networks identified by multi-attribute decision-making method

Abstract

In complex networks, it is significant how to rank the nodes according to their importance. Most of the existing methods of ranking key nodes (e.g. degree-based, betweenness-based) only consider one factor but not the integration of whole complex network in evaluating the importance of nodes, so those methods each have a limited application range. In this paper, a multi-attribute decision-making method to identify the key nodes in complex networks is proposed. In our method, each node is regarded as a solution, and each importance evaluation criterion as one solution's attribute. After that, we calculate the closeness between each solution and the ideal solution in order to obtain the integration results of node importance in complex networks. The proposed method can be used in a variety of complex networks. It is also easy to evaluate the importance evaluation criteria. Finally, experimental results show that the proposed method is effective.