个人信息Personal Information
教授
博士生导师
硕士生导师
性别:女
毕业院校:大连理工大学
学位:博士
所在单位:计算机科学与技术学院
学科:计算机应用技术
电子邮箱:syuan@dlut.edu.cn
A diameter path based method for important node detection in complex network
点击次数:
论文类型:会议论文
发表时间:2017-01-01
收录刊物:CPCI-S
卷号:2017-January
页面范围:5669-5674
关键字:central nodes; centrality; network diameter; edge deleting; complex network
摘要:The strategies for important node detection according to topological structures are widely explored in complex networks. The diameter is a very important topological parameter among various network topological indicators. However, it is seldom utilized in important node searching methods. In this study, we defined the nodes on the diameter paths as central nodes and proposed a Diameter Center Detection (DCD) method to search the central nodes. In the experiments, the DCD method is applied to three deterministic networks, a series of small-world networks, scale-free networks and five real networks, respectively. The experimental results show that the central nodes searched by DCD have advantages over the nodes of the whole network in the evaluations of various centrality measures, e.g. Betweenness Centrality (BC), Closeness Centrality (CC), Degree Centrality (DC) and k-shell decomposition results. In addition, after deleting central nodes, the network structure changes a lot on the perspective from both diameter and giant component. Furthermore, the experimental results show that the edge deleting policy based on DCD is effective in the way that deleting fewer edges disrupting more node pair connectivity.