Identifying vital spreaders in complex networks based on the interpretative structure model and improved Kshell

The identification of vital spreaders in complex networks has been one of the most interesting topics in network science. Several methods were proposed to deal with this challenge, but there still exist deficiencies in previous methods, such as excessive time complexity, inadequate accuracy of recognition results after dividing the topological structure, and the ignorance of neighbors' attribute information in the links' significance model. To address these issues and promote identifying ability more effectively, the proposed extended centrality upon hybrid information, named EISMC, introduces the interpretative structure model (ISM) and improves hierarchical weight results after the division in hierarchies. Based on the hierarchical structure of Improved Kshell decomposition (IKs), the weight value of each layer is updated, and meanwhile the local centrality under link significance (LinkC) is created to supplement local features in this method. In this paper, six real-world networks and nine comparison methods are applied to conduct a series of simulations and tests. Results demonstrate that the proposed method outperforms state-of-the-art algorithms in the identifying effects for good spreading influence.