Identifying influential nodes in complex networks through the k-shell index and neighborhood information

Identifying influential nodes is crucial in network science for controlling diseases, sharing information, and viral marketing. Current methods for finding vital spreaders have problems with accuracy, resolution, or time complexity. To address these limitations, this paper presents a hybrid approach called the Bubble Method (BM). First, the BM assumes a bubble with a radius of two surrounding each node. Then, it extracts various attributes from inside and near the surface of the bubble. These attributes are the k-shell index, k-shell diversity, and the distances of nodes within the bubble from the central node. We compared our method to 12 recent ones, including the Hybrid Global Structure model (HGSM) and Generalized Degree Decomposition (GDD), using the Susceptible-Infectious-Recovered (SIR) model to test its effectiveness. The results show the BM outperforms other methods in terms of accuracy, correctness, and resolution. Its low computational complexity renders it highly suitable for analyzing large-scale networks.