A hybrid recognition framework of crucial seed spreaders in complex networks with neighborhood overlap

Abstract

Recognizing crucial seed spreaders of complex networks is an open issue that studies the dynamic spreading process and analyzes the performance of networks. However, most of the findings design the hierarchical model based on nodes’ degree such as Kshell decomposition for obtaining global information, and identifying effects brought by the weight value of each layer is coarse. In addition, local structural information fails to be effectively captured when neighborhood nodes are sometimes unconnected in the hierarchical structure. To solve these issues, in this paper, we design a novel hierarchical structure based on the shortest path distance by using the interpretative structure model and determine influence weights of each layer. Furthermore, we also design the local neighborhood overlap coefficient and the local index based on the overlap (LIO) by considering two conditions of connected and unconnected neighborhood nodes in the hierarchical structure. For reaching a comprehensive recognition and finding crucial seed spreaders precisely, we introduce influence weights vector, local evaluation index matrix after normalization and the weight vector of local indexes into a new hybrid recognition framework. The proposed method adopts a series of indicators, including the monotonicity relation, Susceptible-Infected-Susceptible model, complementary cumulative distribution function, Kendall’s coefficient, spreading scale ratio and average shortest path length, to execute corresponding experiments and evaluate the diffusion ability in different datasets. Results demonstrate that, our method outperforms involved algorithms in the recognition effects and spreading capability.