Combining Gravity Box-Coverage with Effective Distance to Identify Key Nodes in Complex Networks

Abstract

Contemporary techniques for identifying key nodes in complex networks typically rely on the static topology of the network, often neglecting the potential dynamic information available. We introduce a novel centrality measurement approach named gravity box-coverage and effective distance (GBED). It capitalizes on the notion that the internal structure of the gravity box encapsulates crucial information about nodes. It transforms static Euclidean distance into dynamic effective distance (ED), extracting concealed insights through an analysis of both static and dynamic topological paths. Initially, the ED between nodes is computed based on node arrival probabilities. Subsequently, the box-coverage algorithm defines the influence area of nodes. The improved gravity model is then applied to estimate the interaction ability between nodes. Finally, the local influence capability score of the node's box, covering the influence region, is calculated. The global influence capability score of the node is aggregated according to the neighborhood rule. We compare it with five established methods based on nine real-world networks. In the SIR epidemic spreading, the nodes identified by GBED exhibit a broader range of influence, and the correlation between estimated influences of nodes from GBED and real influences by simulation is higher than correlations associated with other algorithms