Identification of Important Nodes Based on Local Effective Distance-Integrated Gravity Model.

Abstract

The research into complex networks has consistently attracted significant attention, with the identification of important nodes within these networks being one of the central challenges in this field of study. Existing methods for identifying key nodes based on effective distance commonly suffer from high time complexity and often overlook the impact of nodes' multi-attribute characteristics on the identification outcomes. To identify important nodes in complex networks more efficiently and accurately, we propose a novel method that leverages an improved effective distance fusion model to identify important nodes. This method effectively reduces redundant calculations of effective distances by employing an effective-influence node set. Furthermore, it incorporates the multi-attribute characteristics of the nodes, characterizing their propagation capabilities by considering local, global, positional, and clustering information and thereby providing a more comprehensive assessment of node importance within complex networks.