图积的 g 外连接性

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE Journal of Computer and System Sciences Pub Date : 2024-05-29 DOI:10.1016/j.jcss.2024.103552
Zhao Wang , Yaping Mao , Sun-Yuan Hsieh , Ralf Klasing , Yuzhi Xiao
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引用次数: 0

摘要

连接性是互联网络容错的重要参数之一。1996 年,Fàbrega 和 Fiol 提出了 "额外连通性 "的概念。如果一个顶点子集不连通,则称其为一个。如果切割集的每个分量都至少有顶点,则称为切割集,其中为非负整数。如果至少有一个-切集,那么 ,表示为 ,定义为所有-切集的最小心数。 在本文中,我们首先获得了两个一般图的词典乘积的-外部连通性的精确值。接着,我们给出了两个一般图的笛卡尔积的-外连通性的上下限。最后,我们将结果应用于网格图和二维广义超立方体。
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The g-extra connectivity of graph products

Connectivity is one of important parameters for the fault tolerant of an interconnection network. In 1996, Fàbrega and Fiol proposed the concept of g-extra connectivity. A subset of vertices S is said to be a cutset if GS is not connected. A cutset S is called an Rg-cutset, where g is a non-negative integer, if every component of GS has at least g+1 vertices. If G has at least one Rg-cutset, the g-extra connectivity of G, denoted by κg(G), is then defined as the minimum cardinality over all Rg-cutsets of G. In this paper, we first obtain the exact value of g-extra connectivity for the lexicographic product of two general graphs. Next, the upper and lower sharp bounds of g-extra connectivity for the Cartesian product of two general graphs are given. In the end, we apply our results on grid graphs and 2-dimensional generalized hypercubes.

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来源期刊
Journal of Computer and System Sciences
Journal of Computer and System Sciences 工程技术-计算机:理论方法
CiteScore
3.70
自引率
0.00%
发文量
58
审稿时长
68 days
期刊介绍: The Journal of Computer and System Sciences publishes original research papers in computer science and related subjects in system science, with attention to the relevant mathematical theory. Applications-oriented papers may also be accepted and they are expected to contain deep analytic evaluation of the proposed solutions. Research areas include traditional subjects such as: • Theory of algorithms and computability • Formal languages • Automata theory Contemporary subjects such as: • Complexity theory • Algorithmic Complexity • Parallel & distributed computing • Computer networks • Neural networks • Computational learning theory • Database theory & practice • Computer modeling of complex systems • Security and Privacy.
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