论文标题
基于laplacian度的K-均匀超图的极端
Extremality of graph entropy based on Laplacian degrees of k-uniform hypergraphs
论文作者
论文摘要
图熵描述了图的结构信息。由一般图中图熵的定义激励，基于拉普拉斯学位的超图的图形熵被定义。简单图的图熵的一些结果扩展到K-均匀的超图。使用边缘移动操作，基于拉普拉斯度的最大和最小图熵在K-均匀的大型身体，单轮均匀的K-均匀超图，Bicyclic K-均匀的HyperGraphs和K-均匀的化学大体中分别确定，并确定相应的图形。
The graph entropy describes the structural information of graph. Motivated by the definition of graph entropy in general graphs, the graph entropy of hypergraphs based on Laplacian degree are defined. Some results on graph entropy of simple graphs are extended to k-uniform hypergraphs. Using an edge-moving operation, the maximum and minimum graph entropy based on Laplacian degrees are determined in k-uniform hypertrees, unicyclic k-uniform hypergraphs, bicyclic k-uniform hypergraphs and k-uniform chemical hypertrees, respectively, and the corresponding extremal graphs are determined.
