摘要
为有效满足规模和密度快速增长的图数据对可视化结果可读性的需求,本文主要对数据结构可视化的布局算法进行了研究,针对大规模图数据完成了基于模块分析的可视化方法的构建,首先对LPA(Label Propagation Algorithm,标签传播算法)社团发现算法进行改进,再据此完成对大规模图数据的划分,分析划分后各模块的结构模式并将其抽象为节点,最后通过力引导算法的使用完成对处理后模块的布局及模块结构模式信息的展示,有效实现了大规模图数据的可视化处理过程,为进一步处理加工数据提供支撑。
In order to effectively meet the requirement of visual data readability of graph data with rapid growth in scale and density,this paper mainly studies the layout algorithm of data structure visualization, and completes the construction of visualization method based on module analysis for large-scale graph data. Firstly, the LPA community discovery algorithm is improved, and then the large-scale graph data is divided, the structural pattern of each module after partitioning is analyzed and abstracted into nodes, and finally the processed module is completed by the use of the force-guided algorithm. The display of layout and module structure mode information effectively realizes the visualization process of large-scale graph data, and provides support for further processing of processing data.
作者
崔尚勇
CUI Shang-yong(Shaanxi Technical College of Finance&Economics,Xianyang 712000 China)
出处
《自动化技术与应用》
2020年第11期44-47,共4页
Techniques of Automation and Applications
关键词
大规模图数据
社团发现算法
模块结构信息可视化
布局算法
large-scale graph data
community discovery algorithm
module structure information visualization
layout algorithm