A new method of identifying influential nodes in complex networks based on TOPSIS

In complex networks, identifying influential nodes is the very important part of reliability analysis, which has been a key issue in analyzing the structural organization of a network. In this paper, a new evaluation method of node importance in complex networks based on technique for order performance by similarity to ideal solution (TOPSIS) approach is proposed. TOPSIS as a multiple attribute decision making (MADM) technique has been an important branch of decision making since then. In addition, TOPSIS is first applied to identify influential nodes in a complex network in this open issue. In different types of networks in which the information goes by different ways, we consider several different centrality measures as the multi-attribute of complex network in TOPSIS application. TOPSIS is utilized to aggregate the multi-attribute to obtain the evaluation of node importance of each node. It is not limited to only one centrality measure, but considers different centrality measures, because every centrality measure has its own disadvantage and limitation. Then, we use the Susceptible-Infected (SI) model to evaluate the performance. Numerical examples are given to show the efficiency and practicability of the proposed method.