广义导树揭示的图的多粒度性
The Multi-granularity in Graph Revealed by a Generalized Leading Tree
摘要：
网络具有层次性特征，如何有效地揭示网络的层次性特征是网络结构研究中的一个难题。如果一个节点被分配到它所属的社区，如何将该社区分配到它所属的更高级别的社区是一个问题。在本文中，数据点的密度进行了研究的聚类任务的基础上。通过形成数据点的密度，构造数据点的层次差异。结合数据点之间的距离，可以构建基于密度的引导树。但是在图结构中，构建一个引导树来揭示图上节点的层次关系是一个问题。本文在基于密度的树形成方法的基础上，将导树模型扩展到图节点的层次结构，讨论了图节点的重要性，形成了一棵能够揭示图节点层次结构和社区依赖关系的导树。在真实数据集上进行了实验，实验中形成了一个树结构。该图引导树能很好地揭示图结构中的层次关系。
英文摘要：
There are hierarchical characteristics in the network and how to effectively reveal the hierarchical characteristics in the network is a problem in the research of network structure. If a node is assigned to the community to which it belongs, how to assign the community to a higher level of community to which it belongs is a problem. In this paper, the density of data points is investigated based on the clustering task. By forming the density of data points, the hierarchical difference of data points is constructed. In combination with the distance between data points, a density-based leading tree can be constructed. But in a graph structure, it is a problem to build a lead tree that reveals the hierarchical relationships of the nodes on the graph. Based on the method of tree formation based on density, this paper extends the model of leading tree to the hierarchical structure of graph nodes, discusses the importance of graph nodes, and forms a leading tree that can reveal the hierarchical structure of graph nodes and the dependency of community. Experiments were carried out on real data sets, and a tree structure was formed in the experiment. This graph leading tree can well reveal the hierarchical relationships in the graph structure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
