正则图的故障可诊断性

Mei-Mei Gu, Law, Rongxia Hao, E. Cheng
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引用次数: 4

摘要

互联网络的可诊断性是衡量互联网络自诊断能力的重要指标。2012年,Peng等人提出了一种网络故障诊断的度量,即h-好邻居条件可诊断性,它要求每个无故障节点至少有h个无故障邻居。有两种比较知名的诊断模型:PMC模型和MM*模型。PMC下的h-近邻可诊断性。图G的MM*)模型,用tPMC h (G)表示。t MM * h (G))是t的最大值,使得G在PMC (resp。毫米*)模型。本文研究了一类一般k-正则k连通图G在PMC模型和MM*模型下的2近邻可诊断性。主要结果tPMC 2 (G) = t MM∗2 G (G) = (k−1)−1与一些可接受的条件,在G的周长是G .此外,获得以下新结果在两个模型:tPMC 2(小企业)= t MM∗2(小企业)= 4 n−5等级的星形网络HSn、t PMC 2 (2 n) = t MM∗2 (2 n) = 6 n−13 split-star网络S2 n和tPMC 2(Γn(∆))= t MM∗2(Γn(∆))= 6 n−16凯莱图生成的2-treeΓn(∆)。
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Fault diagnosability of regular graphs
An interconnection network’s diagnosability is an important measure of its selfdiagnostic capability. In 2012, Peng et al. proposed a measure for fault diagnosis of the network, namely, the h-good-neighbor conditional diagnosability, which requires that every fault-free node has at least h fault-free neighbors. There are two well-known diagnostic models, PMC model and MM* model. The h-goodneighbor diagnosability under the PMC (resp. MM*) model of a graph G, denoted by tPMC h (G) (resp. t MM∗ h (G)), is the maximum value of t such that G is h-good-neighbor t-diagnosable under the PMC (resp. MM*) model. In this paper, we study the 2-good-neighbor diagnosability of some general k-regular kconnected graphs G under the PMC model and the MM* model. The main result tPMC 2 (G) = t MM∗ 2 (G) = g(k − 1)− 1 with some acceptable conditions is obtained, where g is the girth of G. Furthermore, the following new results under the two models are obtained: tPMC 2 (HSn) = t MM∗ 2 (HSn) = 4n− 5 for the hierarchical star network HSn, t PMC 2 (S 2 n) = t MM∗ 2 (S 2 n) = 6n− 13 for the split-star networks S2 n and tPMC 2 (Γn(∆)) = t MM∗ 2 (Γn(∆)) = 6n − 16 for the Cayley graph generated by the 2-tree Γn(∆).
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来源期刊
Theory and Applications of Graphs
Theory and Applications of Graphs Mathematics-Discrete Mathematics and Combinatorics
CiteScore
0.70
自引率
0.00%
发文量
17
审稿时长
20 weeks
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