一种基于改进 K 核分解的合作网络关键节点集识别方法

[Objective] This paper proposes an improved K-shell decomposition algorithm based on semi-local centrality, aiming to address the degradation issue of critical nodes identification. [Methods] First, we constructed a semi-local centrality index based on the nodes’first-order neighbor information. Then, we determined the final key node set by recursive removal, with the semi-local information of the remaining and removed nodes. [Results] We examined our algorithm with six groups of cooperative networks. It could effectively eliminate the degradation issue of the original algorithm with high computational accuracy and low computational complexity. [Limitations] Due to the influence of network structures, the calculation accuracy of some sample networks was lower than that of the betweenness centrality algorithm. [Conclusions] The new algorithm can improve the stability of the collaboration network and identify key node sets in large-scale practical networks.