论文标题
图形中的多粒性由广义的前导树揭示
The Multi-granularity in Graph Revealed by a Generalized Leading Tree
论文作者
论文摘要
网络中存在分层特征，以及如何有效揭示网络中的层次特征是网络结构研究中的一个问题。如果将节点分配给其所属的社区，则如何将社区分配到其所属的更高水平的社区是一个问题。在本文中，根据聚类任务研究了数据点的密度。通过形成数据点的密度，构建了数据点的层次差异。结合数据点之间的距离，可以构建基于密度的前导树。但是在图形结构中，构建铅树是一个问题，该树揭示了图表上节点的层次关系。基于基于密度的树形成的方法，本文将领先树的模型扩展到了图节点的层次结构，讨论了图节点的重要性，并形成了一个领先的树，可以揭示图形节点的层次结构和社区的依赖性。实验是对实际数据集进行的，并在实验中形成了树结构。该图引导树可以很好地揭示图形结构中的分层关系。
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.
