基于复杂网络多阶邻域贡献度的节点重要性序结构辨识

Identification of Node Importance Order Structure Based on Multi-Order Neighborhood Contribution of Complex Network

为更精细化辨识节点重要性,本文研究节点多阶交互演化对节点重要性序结构形成的影响,提出基于复杂网络多阶邻域贡献度的节点重要性序结构辨识系统模型.首先,基于节点间不同阶层交互关系和节点多阶邻域规模异质性程度构建多阶邻域贡献度模型;通过节点多阶圈信息集结节点自身多阶邻域空间结构信息;其次,构建融合多阶邻域空间位置信息贡献和多阶圈信息贡献的节点重要性辨识系统模型,给出节点序结构辨识算法;最后,仿真分析表明在各真实网络中本文算法相比经典算法最高提升88%节点辨识率,以0.5资源投入进行网络攻击,分别最大提升67.47%,39.40%和20.17%攻击效用值.

In order to finely identify the node importance,this paper studies the influence of node multi-order interac⁃tion evolution on node importance formation,and proposes a system model of node importance sequence structure identifi⁃cation based on multi-order neighborhood contribution of complex networks.Firstly,a multi-order neighborhood contribu⁃tion model is constructed based on different hierarchical interactions between nodes and the degree of heterogeneity of nodes'multi-order neighborhood scale;and characterize the nodes'own multi-order neighborhood spatial structure infor⁃mation by nodes'multi-order circle information.Secondly,a node importance identification model is constructed by inte⁃grating the contributions of multi-order neighborhood spatial location information and multi-order circle information,and a node order structure identification algorithm is given.Finally,the simulation analysis shows that this algorithm improves the node identification rate by up to 88%compared with the classical algorithm in each real network,and improves the at⁃tack utility value by up to 67.47%,39.40%and 20.17%with 0.5 resource input for network attacks,respectively.