Identifying Critical Nodes of Collaboration Networks Based on Improved K-shell Decomposition
[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.
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