A Novel Model to Identify the Influential Nodes: Evidence Theory Centrality

The identification of critical nodes in complex networks is an open issue. Many scholars have tried to address it from different perspectives, but their methods are often not as effective as usual especially when meeting some specific graphs or limited to only one aspect. Evidence theory can consider the results from different sources comprehensively and the Shannon entropy can measure the uncertainty of information. In this paper, we use these two methods to rate the results gained from different measures and combine them to generate a new ranking result, namely Evidence Theory Centrality (ETC). The Susceptible-infected (SI) model and Kendall’s tau coefficient are used on six real networks to examine the effectiveness of our method.