Evaluating node importance in complex networks based on TOPSIS and gray correlation

The importance evaluation of nodes is a key problem in complex network research. It has great application prospects for the mining of large V network, the locking of network virus sources and the directional intervention of network public sentiment. The difficulty of the study is how to truly reflect the importance of the nodes in the network. The existing importance evaluation scheme has a limited range of adaption, and there is a large discrepancy between the evaluation results and the actual situation. This paper combines the Technique by Similarity Solution (TOPSIS) and Gray Correlation, taking advantage of TOPSIS' decision making method for high accuracy and Gray Correlation for poor information. In the experiment using the relationship network of the novel "Les Miserables" characters to verify the scheme, and comparing with TOPSIS and PageRank, the result shows that the plan is in accordance with the actual situation, and has high accuracy.