Identifying influential spreaders in complex networks based on local and global structure

Complex systems intricately intertwine with life, and the identification of the most influential spreaders in complex networks can aid in resolving numerous pragmatic problems. Nevertheless, the identification of such kinds of nodes currently stands as an open and challenging issue. In order to accurately and efficiently address this issue, numerous metrics have been proposed. In this paper, we propose a new method based on degree, clustering coefficient and k-shell decomposition value—$DCK$ to detect the most influential spreaders by gauging the spreading ability of nodes. The proposed centrality assesses the significance of a node by the impacts of its neighbors, encompassing both the local and global network structures. To evaluate the performance of $DCK$, we compare it with different centrality measures under utilizing the Susceptible–Infected–Recovered model to simulate the propagation of epidemics across real-world networks. Experiments on real networks illustrate that $DCK$ exhibits superior differentiation ability and more accurate identification ability for influential spreaders and compared with other methods, Kendall’s $\tau$ correlation coefficient of the $DCK$ could be enhanced by 12.82%, 13.20%, 8.62%, 5.32%, 7.97% and 11.73% for the degree centrality, K-shell decomposition, $GLI$ centrality, $H$-$GSM$ centrality, $LGI$ centrality and $NPCC$ centrality.