一种基于改进K核分解的合作网络关键节点集识别方法

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

【目的】针对关键节点集识别算法中广泛存在的退化性问题,提出一种以半局域中心性为基础的改进型K-shell分解算法。【方法】算法根据节点一阶邻居信息构建半局域中心性指标,在考虑剩余节点的半局域信息和已移除节点的半局域信息基础上,通过递归移除方式确定最终的关键节点集。【结果】6组实际合作网络数据实验表明,改进的K-shell分解算法能够有效消除原有算法中的退化性问题,具有较高的计算准确性和较低的计算复杂度,适用于大规模合作网络中关键节点集的识别。【局限】受网络结构属性的影响,在部分样本网络中计算准确性低于介数中心性方法。【结论】通过对改进的K-shell分解算法计算所得的核心节点集的有效保护,能够提升合作网络的稳定性,有利于合作网络目标的实现。

[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.