A modified weighted TOPSIS to identify influential nodes in complex networks

Identifying influential nodes in complex networks is still an open issue. Although various centrality measures have been proposed to address this problem, such as degree, betweenness, and closeness centralities, they all have some limitations. Recently, technique for order performance by similarity to ideal solution (TOPSIS), as a tradeoff between the existing metrics, has been proposed to rank nodes effectively and efficiently. It regards the centrality measures as the multi-attribute of the complex network and connects the multi-attribute to synthesize the evaluation of node importance of each node. However, each attribute plays an equally important part in this method, which is not reasonable. In this paper, we improve the method to ranking the node’s spreading ability. A new method, named as weighted technique for order performance by similarity to ideal solution (weighted TOPSIS) is proposed. In our method, we not only consider different centrality measures as the multi-attribute to the network, but also propose a new algorithm to calculate the weight of each attribute. To evaluate the performance of our method, we use the Susceptible–Infected–Recovered (SIR) model to do the simulation on four real networks. The experiments on four real networks show that the proposed method can rank the spreading ability of nodes more accurately than the original method.