基于改进k-shell算法的节点影响力的识别  

Identification of Node Influence Based on Improved k-shell Algorithm

作　　者：朱晓霞[1] 胡小雪 ZHU Xiaoxia;HU Xiaoxue(School of Economics and Management,Yanshan University,Qinhuangdao,Hebei 066004,China)

机构地区：[1]燕山大学经济管理学院,河北秦皇岛066004

出　　处：《计算机工程与应用》2019年第1期35-41,共7页Computer Engineering and Applications

基　　金：国家自然科学基金(No.71301140);河北省自然科学基金(No.G2015203425);河北省三三三人才工程项目(No.A2016002038);河北省青年拔尖人才计划(No.BJ2017078)

摘　　要：在复杂网络中具有较大影响力的节点在控制谣言传播、优化资源分配、高效传播信息、精确投放广告等方面发挥着重要作用。鉴于当前众多方法在识别节点的不同影响力时存在一定局限性,因此在k-shell方法的基础上,通过度量边的潜在重要性,考虑邻居节点的差异贡献性,从而定义了节点的加权度概念,并提出了MKS(Modified k-shell)算法,该算法综合考虑了节点的本身、位置以及局部属性。通过在具有代表性的Zachary空手道俱乐部网络上进行实现,并和其他典型方法进行比较分析,发现该算法改进了k-shell方法的粗粒化划分,其结果更加合理。Nodes that have greater influence in complex networks play an important role in controlling rumors propagation,optimizing resource allocation, spreading information efficiently, and advertising accurately. In view of the current many methods in identifying the node’s influence have certain limitation, this paper based on the k-shell algorithm defines the concept of weighted degree, and puts forward the Modified k-shell(MKS)algorithm, shorted for MKS algorithm by measuring the potential importance of edges and considering the different contributions of neighbors. This algorithm considers the nodes’ own features, location features and local features. Through implementing this algorithm on the representative Zachary karate club network and comparing with other typical methods, it is found that this algorithm improves the coarse division of k-shell algorithm, and its result is more reasonable.

关 键 词：复杂网络 K-SHELL 加权度 影响力识别 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]