A Key Node Identification Algorithm in Social Networks Based on Global Betweenness Entropy

As social networks continue to develop, accurately identifying key nodes has become crucial. Existing centrality algorithms often rely on local network structures and are susceptible to changes in the degree of individual nodes, making it difficult to identify truly important nodes from a global perspective. This paper proposes an algorithm based on an improved Global Betweenness Degree Entropy (GBDE) to identify key nodes in complex networks. The algorithm combines closeness centrality and the eigenvector centrality of neighboring nodes, iteratively selecting and optimizing the node set to ultimately identify nodes that are critical to the network structure and the propagation process. This algorithm not only considers the network position of the nodes but also takes into account their relationships with adjacent nodes, thereby improving the accuracy and effectiveness of key node identification. Experimental results on five real-world networks demonstrate that the GBDE algorithm outperforms BDE, DC, BC, LE, and ME algorithms in the task of identifying key nodes.