基于精准k核的复杂网络节点重要性评估方法

由于k核存在破坏网络整体结构信息、忽略邻居节点影响力等缺点,导致每个节点难以量化区分.为了提高关键节点的识别精度,首先改进了k核的分解过程,提出了精准k核Ak.考虑到网络中局部特征信息和全局结构信息对节点的影响,将精准k核应用到重力中心性中,并提出了精准重力中心性AGC.信息学中的香农熵在网络关键节点识上具有良好的扩展性,通过结合邻域度中心性、邻域精准k核以及精准重力中心性三者的香农熵,最终提出了混合中心性MC对节点重要性进行多元评估.在7种真实网络下,对MC和其他节点评估指标分别从单调性和准确性上进行了一系列实验,实验结果表明MC具有更好的关键节点识别性能.

An evaluation method of critical nodes in complex network based on accurate k-shell

Critical nodes are the core elements of complex networks and can play a critical role in maintaining structural stability and information transmission. The k-shell is a common measure index of node importance. But due to its shortcomings such as destroying the overall structure information of the network and ignoring the influence of neighboring nodes, it is difficult to ensure that each node can be quantitatively distinguished. In order to improve the accuracy of node identification, this paper firstly improves the decomposition process of k-shell and proposes the Ak (accurate k-shell). Considering the influence of local feature information and global structure information on nodes in the network, the Ak is applied to the gravity centrality and the AGC (accurate gravity centrality) is proposed, subsequently. Because of the good expansibility for Shannon entropy in informatics has good expansibility in the identification of key nodes in the network,the MC (mixed centrality) is finally proposed to evaluate the importance of nodes pluralistically by combining Shannon entropy of neighborhood centrality, neighborhood Ak and AGC. Under 7 kinds of real networks, a series of experiments on the monotonicity and accuracy of MC and other node evaluation indexes in terms of monotonicity and accuracy were conducted. The experimental results show that MC has better performance in identifying key nodes.