关于图的非正常边着色

图Ｇ的非正常边着色，即（ｍ·ｄ）一边着色是把边集Ｅ（Ｇ）划分成ｍ个子集Ｅ１，Ｅ２，…，Ｅｍ，使得每一边子集的导出子图Ｇ〔Ｅｉ〕，ｉ＝１，２，…，ｍ的最大度最多是ｄ。Ｗｏｏｄａｌ问：对奇数ｄ和自然数ｍ，最大度是ｍｄ的第二类图中哪些是（ｍｄ）一边可着色的？哪些不是？本文对Ｗｏｏｄａｌ的这一公开问题给出了一些明确的解答。     

一类K4—同胚图的色唯一性

本文解决了文献〔１〕中的问题１５，即给出了Ｋ４－同胚图Ｋ４（２，β，１，１，１，η）不是色唯一的充分必要条件。     

轮和路的广义Mycielski图的星全染色

图G的一个正常全染色被称作G的星全染色,如果G中任意路长为2的点和边着色均不相同.图的全部星k-全着色中最小的数k称为它的星全色数.讨论轮和路的广义Mycielski图的星全染色问题,得到不同情况下它们的星全色数,其中每个点的色集合包含该点及其关联边的颜色.     

一类图(C)2n的优美性

给出了图(C)2n的定义,并对其优美标号进行研究,得到了当n=4k+1(k≥1)时,图(C)2n是优美图的结论.     

LEARNING CAUSAL GRAPHS OF NONLINEAR STRUCTURAL VECTOR AUTOREGRESSIVE MODEL USING INFORMATION THEORY CRITERIA

Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis. Traditional causality inference methods have a salient limitation that the model must be linear and with Gaussian noise. Although additive model regression can effectively infer the nonlinear causal relationships of additive nonlinear time series, it suffers from the limitation that contemporaneous causal relationships of variables must be linear and not always valid to test conditional independence relations. This paper provides a nonparametric method that employs both mutual information and conditional mutual information to identify causal structure of a class of nonlinear time series models, which extends the additive nonlinear times series to nonlinear structural vector autoregressive models. An algorithm is developed to learn the contemporaneous and the lagged causal relationships of variables. Simulations demonstrate the effectiveness of the nroosed method.     

图 S＊的边幻和标号以及超边幻和标号

研究了对？n∈N＊图 S＊的边幻和标号以及超边幻和标号,得到了两种标号的算法 A 和 B,给出了对？n∈N＊图 S＊具有超边幻和常数 C1=5n＋6以及边幻和常数 C2=7n＋6,其中图 S＊由具有 n＋1个顶点星图 S（u）和 n＋1个顶点星图 S（v）组成,从而证明了 S＊不仅是边幻和图,而且还是超边幻和图等结论。     

三类组合图的伴随多项式的因式分解及色性

通过研究星图Sn 1的三类组合图的伴随多项式的因式分解,证明了这三种图的补图的色等价图的特征性质.     

Curie’s principle and causal graphs

Curie’s Principle says that any symmetry property of a cause must be found in its effect. In this article, I consider Curie’s Principle from the point of view of graphical causal models, and demonstrate that, under one definition of a symmetry transformation, the causal modeling framework does not require anything like Curie’s Principle to be true. On another definition of a symmetry transformation, the graphical causal modeling formalism does imply a version of Curie’s Principle. These results yield a better understanding of the logical landscape with respect to the relationship between Curie’s Principle and graphical causal modeling.     

极小3连通图的非基本边数

利用扇,断片及简约图的概念,得到不为轮的极小3连通图的非基本边数与其简约图的非基本边数相等,从而将求极小3连通图的非基本边数问题转化为求其简约图的非基本边数问题后,给出简约极小3连通图非基本边数的一个下界,刻画了达到下界的图类.     

一类特殊的Kautz无向图的限制边连通度

限制边连通度是传统边连通度的推广,而且是计算机互连网络容错性的一个重要度量．该文考虑Kautz无向图UK(3,n)的限制边连通度λ’,得到如下结果：λ'(UK(3,1))=4,n≥2时,λ'(UK(3,n))=8.