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.     

扇图与轮图的边平均Wiener指标及其算法

文章证明了扇图Fn和轮图Wn的边平均Wiener指标,并给出扇图中任意两边之间的平均距离的算法程序.     

单圈图的k-距离匹配控制数

单圈图是边数等于顶点数的连通图.令G=(V,E)是无孤立顶点的图,若集合DV(G)是G的一个k-距离控制集且导出子图〈D〉有完美匹配,则称D是G的一个k-距离匹配控制集.k-距离匹配控制数γkp(G)是G的最小k-距离匹配控制集的势.主要证明了单圈图k-距离匹配控制数的一个重要引理,由此找到了单圈图k-距离匹配控制数的上界,并构造了极图.     

图C~2_n的优美性

给出了图C2n的定义,并对其优美标号进行研究,得到了当n=4k(k≥2)时图C2n是优美图的结论。     

不含3,4圈的平面图的无圈边染色的一个结果

利用差值转移方法研究了不含3圈,4圈的平面图的无圈边染色,证得了它们的无圈边色数不超过Δ(G)+2。     

A new class of antimagic join graphs

A labeling/of a graph G is a bijection from its edge set E(G) to the set {1,2,…,|E(G)|},which is antimagic if for any distinct vertices x anAy,the sum of the labels on edges incident to x is different from the sum of the labels on edges incident to y.A graph G is antimagic if G has an f which is antimagic.Hartsfield and Ringel conjectured in 1990 that every connected graph other than K_2 is antimagic.In this paper,we show that if G_1 is an m-vertex graph with maximum degree at most 6r+l,and G_2 is an n-vertex(2r)-regular graph(m≥n≥3),then the join graph G_1 v G_2 is antimagic.     

按Laplace谱半径对一些偶单圈图的排序

研究一些偶单圈图按其Laplace谱半径排序的问题.利用扩圈变换对偶单圈图S1k的Laplace谱半径影响的证明方法,得出了顶点数为k+2,圈长为k(k≥10)的偶单圈图C(k+2,k)按其Laplace谱半径从大到小的顺序依次排在前三位和最后一位的单圈图.     

无三角形3-正则图的几个参数的界

图G的一条边称为割边是指删去该边后,使得余下的图的连通分支数增加。图G中的一个两两不相邻的边子集称为图G的一个匹配。图G的一个最大匹配的边数称为图G的匹配数。图G中的一个与G的每个团都有交的顶点子集称为G的一个团横贯集,图G中元素个数最少的团横贯集的顶点数称为G的团横贯数。本文针对n阶连通无三角形的3一正则图G-（V（G）,E（G））,首先给出了其割边数的一个上界（n—l0）／4;其次对它的匹配数得到了一个下界（11n-2）／24;再次对它的线图的团横贯数呈现了一个上界（13｜E（G）｜＋3）／36。同时刻画了达到这些界的极值图。     

关于圈对完全图的多色Ramsey数

证明了关于k个偶圈对完全图的多色Ramsey数的上界.     

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.