基于改进K-Shell的社会网络关键节点挖掘算法

KEY NODES IDENTIFICATION METHOD IN SOCIAL NETWORKS BASED ON IMPROVED K-SHELL

传统的K-Shell分解法具有时间复杂度低的特点,但其划分结果普遍粗粒化,难以满足精细化节点重要性划分的实际需求。基于K-Shell分解法,提出一种改进的重要节点挖掘算法。在充分利用节点的网络位置信息的基础上,考虑节点的度数和节点被删除时所处的迭代层数,提出改进的K-Shell方法;在用改进的K-Shell对节点排名并提取核心网络后,结合节点的PageRank值,定量分析网络核心层的节点,形成多层级的节点重要性划分。在三种真实网络数据集中的实验验证表明,该方法能显著提高K-Shell分解法的分辨率,并且时间复杂度低,适用于大规模网络的应用。

The traditional K-Shell decomposition method has the characteristics of low time complexity,but its division results are generally coarse-grained,which is difficult to meet the actual needs of refined node importance division.This paper proposes an improved key node mining algorithm based on the K-Shell decomposition method.On the basis of fully utilizing the network location information of nodes,by considering both node s degree and its iteration number at which nodes were deleted,an improved K-Shell method was proposed.The core network was extracted based on the improved K-Shell,and PageRank value was used to quantitatively analyze nodes in the core network layer and form a multi-level node importance division.The simulation results on three different real networks data sets show that the proposed method can significantly improve the resolution of K-Shell decomposition method,and has low time complexity,which is suitable for large-scale network applications.