面向特征的网络空间点群要素自动综合方法

Automatic Generalization Methods of Cyberspace Point Cluster Features Considering Characteristics

网络空间信息可视化对揭示网络空域规律、促进网络空间认知具有重要意义。将网络空间节点与拓扑关系直接可视化的视图中存在大量的点重合和线交叉,目前已有的网络节点布局算法、集束边技术、骨干网提取和网络路由拓扑多尺度表达等方法能够优化视图效果,但在网络的微观结构上,对保持网络空间点群要素的特征信息关注不够。通过分析并量化网络空间点群要素的各类特征信息,提出了一种基于层次聚类的要素聚合方法和一种基于节点重要性度量的要素选取方法,以自动综合的方式对网络空间点群要素进行综合。实验结果表明,该方法能够保持网络空间点群要素的空间特征,为定量表达网络空间特征、加速生成视觉效果良好的网络空间地图提供基础数据综合方法。

Objectives:The visualization of cyberspace information is of great significance to reveal the rules and promote the cognition of cyberspace.Visualizing the nodes and their topological relationships in cyberspace without preprocessing often result in a large number of point coincidences and line crossings.Although the visualization result can be optimized by the node layout algorithms,the divided edge bundling technologies,the backbone extraction technologies and the methods of multi-scale representation of network routing,the spatial characteristic information of these nodes as cyberspace point cluster features cannot be maintained on a micro level of network structure.Therefore,by analyzing and quantifying various types of characteristic information of point cluster features in cyberspace from four aspects of statistical information,metric information,topology information and thematic information,two generalization methods are proposed.Methods:(1)An aggregation method based on hierarchical clustering.First,organize the point cluster features into a hierarchical tree of which every final node community is in an appropriate and well-balanced size through multi-layer K-means clustering according to their topology information.Then aggregate these features by their community.(2)A selection method based on node importance measuring.After measuring the comprehensive importance of the each feature from the aspects of maintaining the structural stability and its service coverage area,select the more important ones and delete the relatively unimportant considering the whole network topology.Results:A series of generalization experiments at different spatial scales were performed on the point cluster features incyberspace of an area in a central part of China through these two methods of aggregation and selection.The result shows that these methods can realize the automatic generalization of the point cluster features in cyberspace at different spatial scales while maintaining the spatial characteristics of these features.Conclusions:The purpose is to provide an effective data processing method of quantitatively expressing the characteristics of cyberspace and accelerating the process of optimizing visualization results.