The (Δ+2,2)-incidence coloring of outerplanar graphs

An incidence coloring of graph G is a coloring of its incidences in which neighborly incidences are assigned different colors. In this paper, the incidence coloring of outerplanar graphs is discussed using the techniques of exchanging colors and the double inductions from the aspect of configuration property. Results show that there exists a (Δ+2,2)-incidence coloring in every outerplanar graph, where Δis the maximum degree of outerplanar graph.     

The (Δ+2,2)-incidence coloring of outerplanar graphs

An incidence coloring of graph G is a coloring of its incidences in which neighborly incidences are assigned different colors. In this paper, the incidence coloring of outerplanar graphs is discussed using the techniques of exchanging colors and the double inductions from the aspect of configuration property. Results show that there exists a (Δ+2,2)-incidence coloring in every outerplanar graph, where Δis the maximum degree of outerplanar graph.     

A new DNA algorithm to solve graph coloring problem

Using a small quantity of DNA molecules and little experimental time to solve complex problems successfully is a goal of DNA computing. Some NP-hard problems have been solved by DNA computing with lower time complexity than conventional computing. However, this advantage often brings higher space complexity and needs a large number of DNA encoding molecules. One example is graph coloring problem. Current DNA algorithms need exponentially increasing DNA encoding strands with the growing of problem size. Here we propose a new DNA algorithm of graph coloring problem based on the proof of four-color theorem. This algorithm has good properties of needing a relatively small number of operations in polynomial time and needing a small number of DNA encoding molecules (we need only 6R DNA encoding molecules if the number of regions in a graph is R).     

A new DNA algorithm to solve graph coloring problem

Using a small quantity of DNA molecules and little experimental time to solve complex problems successfully is a goal of DNA computing. Some NP-hard problems have been solved by DNA computing with lower time complexity than conventional computing. However, this advantage often brings higher space complexity and needs a large number of DNA encoding molecules. One example is graph coloring problem. Current DNA algorithms need exponentially increasing DNA encoding strands with the growing of problem size. Here we propose a new DNA algorithm of graph coloring problem based on the proof of four-color theorem. This algorithm has good properties of needing a relatively small number of operations in polynomial time and needing a small number of DNA encoding molecules (we need only 6R DNA encoding molecules if the number of regions in a graph is R).     

Modified PSO algorithm for solving planar graph coloring problem

The graph coloring is a classic NP-complete problem. Presently there is no effective method to solve this problem. Here we propose a modified particle swarm optimization （PSO） algorithm in which a disturbance factor is added to a particle swarm optimizer for improving its performance. When the current global best solution cannot be updated in a certain time period that is longer than the disturbance factor, a certain number of particles will be chosen according to probability and their velocities will be reset to force the particle swarm to get rid of local minimizers. It is found that this operation is helpful to improve the performance of particle swarm. Classic planar graph coloring problem is resolved by using modified particle swarm optimization algorithm. Numerical simulation results show that the performance＇of the modified PSO is superior to that of the classical PSO.     

A probabilistic paradigm for handling uncertain objects in GIS by randomized graph algebra

Probability theory faces difficulties when it is applied to describing uncertain objects in geographic information system (GIS). This is mainly due to the fact that an object in GIS is normally described by a series of discrete vertexes. Modeling uncertainty objects should be therefore based on error of the composed vertexes. This type of model is normally complex and relatively difficult to implement because of many unknown factors, such as the number of vertexes of a polygon, error nature of each individual vertex and error correlation among the vertexes. In this paper, a probabilistic paradigm for handling uncertain objects in GIS by randomized graph algebra is presented. The theoretical basis for this paradigm is the randomized graph algebra-a probability theory for graph-which is newly proposed in this study. Classical probability theory is based on numerical algebra and is also an extension of numerical algebra by further defining probability density within a numerical domain. In the same token, this study begins with defining graph algebra as the basis for probability theory for graph. First, we adopt the theory of graph algebra and further refine the theory by defining the modulo operation for graph. As a result, a graph can thereafter be treated as a "number" and operated by "addition", "subtraction" and others. Second, we construct a measure space by generating sigma-algebra and defining measurable function upon it. The measure space becomes a probability space when the measurable function is a probability density function. Third, we propose the probabilistic paradigm for describing and inferring the uncertainty of geometric objects in GIS by applying the developed randomized graph algebra.     

A-Spectra and Q-Spectra of Two Classes of Corona Graphs

It is well known that graph spectra store a lot of structural information about a graph,and it is more difficult to compute the spectra of corona graphs. In this paper two classes of new corona graphs,the corona-vertex of the subdivision graph G1◇G2and corona-edge of the subdivision graph G1☆G2were defined. Then,by using the coronal of a graph and some knowledge of linear algebra,the adjacency spectra and the signless Laplacian spectra of the two new graphs were explicitly computed in terms of the corresponding spectra of G1 and G2. As the application,some Aintegral graphs were constructed.     

A Graph Drawing Algorithm for Visualizing Multivariate Categorical Data

In this paper, a new approach for visualizing multivariate categorical data is presented. The approach uses a graph to represent multivariate categorical data and draws the graph in such a way that we can identify patterns, trends and relationship within the data. A mathematical model for the graph layout problem is deduced and a spectral graph drawing algorithm for visualizing multivariate categorical data is proposed. The experiments show that the drawings by the algorithm well capture the structures of multivariate categorical data and the computing speed is fast.     

Saturation Number for Linear Forest 2P_3∪tP_2

For a fixed graph F,a graph G is F-saturated if it has no F as a subgraph,but does contain F after the addition of any new edge.The saturation number,sat(n,F),is the minimum number of edges of a graph in the set of all F-saturated graphs with order n.In this paper,we determine the saturation number sat(n,2 P_3∪tP_2)and characterize the extremal graphs for n≥6 t+8.     

An Efficient Algorithm for Discovering Co-occurrence Concepts Through Pathfinder Paradigm

The Pathfinder paradigm has been used in generating and analyzing graph models that support clustering similar concepts and minimum-cost paths to provide an associative network structure within a domain. The co-occurrence pathfinder network （ CPFN ） extends the traditional pathfinder paradigm so that co-occurring concepts can be calculated at each sampling time. Existing algorithms take O（n（s）） time to calculate the pathfinder network （PFN） at each sampling time for a non-completed input graph of a CPFN （r = ∞, q = n - 1）, where n is the number of nodes in the input graph, r is the Minkowski exponent and q is the maximum number of links considered in finding a minimum cost path between vertices. To reduce the complexity of calculating the CPFN, we propose a greedy based algorithm, MEC（G） algorithm, which takes shortcuts to avoid unnecessary steps in the existing algorithms, to correctly calculate a CPFN （r = ∞, q= n - 1） in O（klogk） time where k is the number of edges of the input graph. Our example demonstrates the efficiency and correctness of the proposed MEC（G） algorithm, confirming our mathematic analysis on this algorithm.     

The(△+2,2)-incidence coloring of outerplanar graphs

An incidence coloring of graph G is a coloring of its incidences in which neighbody incidences are assigned different colors.In this paper,the incidence coloring of outerplanar graphs is discussed using the techniques of exchanging colors and the double inductions from the aspect of configuration property.Results show that there exists a(△ 2,2)-incidence coloring in every outerplanar graph,where △ is the maximum degree of outerplanar graph.     

图的最大平均度与关联色数

图G的关联着色是从关联集I(G)到颜色集C的一个映射使得任意两个相邻的关联不着同色。从图的结构性质出发,对图的关联着色进行了讨论,利用归纳法和换色技巧证明了mad(G)<3,Δ(G)=4的图G存在一个(6,2)-关联着色。     

无K4-子式图的2-距离和可区别边染色

图G的一个正常边染色φ若满足:∠u,v∈V(G),且d_G(u,v)≤2都有f(u)≠f(v),其中f(u)=∑_uw∈E(G)φ(uw),则称φ为图G的2-距离和可区别边染色。运用反证法,结合构造染色函数法,研究了无K₄-子式图的2-距离和可区别边染色,确定了无K₄-子式图的2-距离和可区别边色数的一个上界。     

最大度为4的外平面图的无圈边色数

一个图G的无圈边染色是一个正常的边染色,使得任一个圈上至少有3种不同的颜色.G的无圈边色数a＇（G）是使得G有无圈k-边染色的最小整数k.设G是一个最大度为4的外平面图.对于现有结果 4≤a＇（G）≤5中,何时为4,何时为5,还没有一个完整的刻画.给出一个使得a＇（G）=4的充分条件,拓展了该领域的相关结果.     

完全二部图K10,n(10≤n≤90)的点可区别E-全染色

图G的一个E-全染色f是指使相邻点染以不同颜色且每条关联边与它的端点染以不同颜色的全染色。对图G的一个E-全染色f,一旦∠u,v∈V(G), u≠v,就有C(u)≠C(v),其中C(x)表示在f下点x的颜色以及与x关联的边的色所构成的集合,则f称为图G的点可区别的E-全染色,简称为VDET染色。令χ^e_vt(G)=min{k|G存在k-VDET染色},称χ^e_vt(G)为图G的点可区别E-全色数。利用分析法和反证法,讨论并给出了完全二部图K_10,n(10≤n≤90)的点可区别E-全色数。     

一类外平面图的有界染色

图G的k-有界染色是图G的一个最多有k个顶点染同一种颜色的顶点染色．图G的k-有界染色数χk(G)是指对图G进行k-有界染色所用的最少颜色数．讨论一类外平面图的k-有界染色，给出能在多项式时间内确定其k-有界染色数的一些充分条件．     

无4-圈和5-圈的平面图的k-frugal列表染色

在图G的一个正常点染色c中,对于图中任意一点v,如果每种颜色在点v的邻点中至多出现k-1次,这个染色就称为图G的一个k-frugal染色。关于无4-圈和5-圈的平面图的k-frugal列表染色问题,有以下两个结论:(1)对于一切不含4-圈和5-圈的平面图,如果其最大度满足Δ≥3k+8,其k-frugal列表色数小于等于「Δ/(k-1)+2;(2)一切不含4-圈和5-圈的平面图,则其k-frugal列表色数小于等于「Δ/(k-1)+5。     

无相交三角形平面图的邻点可区别边染色

图G的k-邻点可区别边染色是指G的一个正常k-边染色满足对任意相邻顶点u和v,与u关联的边所染颜色集合和与v关联的边所染颜色集合不同。使G有k-邻点可区别边染色的k的最小值称为G的邻点可区别边色数,记作χ'_a(G)。通过运用权转移方法研究了无相交三角形平面图的邻点可区别边色数,证明了若图G为无相交三角形平面图,则χ'_a(G)≤max{Δ(G)+2,10}。     

2-连通外部平面图的平方图的列表染色

图G的平方图,记作G2,是一个以原图的顶点集作为顶点集,若原图中两点的距离不大于2则连以边所成的图.图G的列表染色数,记作lχ(G),定义为最小的自然数k,使得满足:对任一顶点给定k种颜色的列表,且染色时每个顶点的颜色只能从自身的颜色列表中选择时,总存在G顶点的一个正常染色.设G是一个最大度为Δ(G)的2-连通外部平面图,则lχ(G2)≤Δ(G)+2.     

关于几类图的邻点可区别关联色数

邻点可区别关联着色的定义是在关联着色的基础上提出的,是使得相邻顶点的颜色集不同的关联着色。主要研究了几类特殊图的邻点可区别关联色数,包括风车图、齿轮图及在此基础上扩充的图Dm、n,拓展了图着色的领域,便于更好地研究图的结构。