双圈图的优美性

针对双圈图, 设计一种图的优美性判定算法, 并对17个点内的所有双圈图进行优美性验证, 得到了该范围内所有的优美图和非优美图. 结果表明, 在17个顶点范围内, 除∞ 型双圈图C_(m,n)外, 其余所有双圈图都是优美的, 其中(m+n)(mod 4)={1,2}. 最后给出该类图的非优美证明, 并进一步猜测当顶点数大于17时, 该结论仍成立.     

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.     

两类粘合图的 Wiener与 Harary指数

连通图G的Wiener指数是指图G中所有点对的距离之和,Harary指数是指图G中所有点对的距离的倒数之和。本文主要研究了单圈图与双圈图的粘合图以及双圈图与双圈图的粘合图的Wiener指数的下界和Harary指数的上界的问题,并刻画了对应的极值图。     

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.     

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.     

图的扩张因子

图的扩张因子是度量图的连通性的一个重要参数.得到了图的扩张因子的上界和下界,刻画了达到上界与下界的图类,给出了n-维交叉超立方体和n-维增广立方体网络的扩张因子.     

一个本原图的scrambling指数

若存在非负整数k（k〉0）,D中的每一对顶点（u,v）都存在另一顶点w,使得从w到u和v都有长为k的途径,且这个k是所有满足上述条件中的最小的整数,就是D的scram-bling指数,记作k（D）.文章给出了一个特殊的本原图的scrambling指数.     

k-连通的强-［k+4,2］图的Hamilton路

如果G的任意s个点的导出子图中至少含有t条独立边,则称图G为强-［s,t］图。本文证明了以下结果：设G是k-连通的强-［k+4,2］图,且δ≥k+1,则G或者有Hamilton路或者同构于(∪^k+2_i=1H_i)∨G_k,其中H_i≌K₂,i=1,2…k+2,G_k是含有k个点的任意图。     

2-连通P3-支配图的Hamilton圈

如果图G中任意一对距离为2的顶点x,y,有J(x,y)∪J′(x,y)≠Φ,则称G为P₃-支配图。本文证明了：设G是n(≥3)阶2-连通P₃-支配图,如果对G中任意一对不相邻的顶点x,y,有2|N(x)∪N(y)|+d(x)+d(y)≥2n-5,则G含有Hamilton圈或者G∈{K_2,3,K_1,1,3}。     

双圈图的Laplacian谱展

设G是一个简单连通图,矩阵L(G)=D(G)-A(G)称为图的Laplacian矩阵,其中D(G)是图的度对角线矩阵,A(G)是G的邻接矩阵.连通图G的Laplacian谱展是图的最大特征值与次小特征值之差.边数等于顶点数加1的连通图叫做双圈图.研究了双圈图的Laplacian谱展,并确定了具有最大Laplacian谱展的双圈图.