Identifying influential nodes in complex networks by adjusted feature contributions and neighborhood impact

Determining the spreading ability of nodes is considered a fundamental issue in network science, with numerous applications in controlling system failure, rumors spreading, and product advertising. Many methods have been proposed to identify influential nodes, which, despite their advantages, suffer from high time complexity, low accuracy, and low resolution. This paper presents a feature based on K-Shell and the degree applied to the node and its neighbors. It adjusts the contribution of various features. The number of selected neighbors and the influence of each neighbor are chosen according to the structural features of the graph. The actual spreading ability of the node is measured with the Susceptible-Infected-Recovered (SIR) model, and the evaluations include accuracy, precision, resolution, correlation, Kolmogorov-Smirnov Test, and time complexity. Assessing 14 real-world and 20 artificial networks compared to 12 recent methods, such as the HGSM (Hybrid Global Structure Model), indicates that the proposed method performs best in various aspects.