Diagnosability of multigraph composition networks

被引：0
作者：
Qin, Xiao-Wen [1 ]
Hao, Rong-Xia [2 ]
Peng, Sheng-Lung [3 ]
机构：
[1] Beijing Univ Chem Technol, Coll Math & Phys, Beijing, Peoples R China
[2] Beijing Jiaotong Univ, Sch Math & Stat, Beijing, Peoples R China
[3] Natl Taipei Univ Business, Dept Prod Innovat & Entrepreneurship, Taoyuan, Taiwan
来源：
THEORETICAL COMPUTER SCIENCE | 2024年 / 988卷
基金：
中国博士后科学基金;
关键词：
Diagnosability; Multigraph alternating composition networks; PMC model; MM* model; MATCHING COMPOSITION NETWORKS; CONDITIONAL DIAGNOSABILITY; DIAGNOSIS; SYSTEMS; FAMILIES;
D O I：
10.1016/j.tcs.2023.114375
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A common approach to scale up a network is by connecting several small-scale networks to form a larger network. A multigraph composition network is a typical network structure obtained by adding an edge set between several connected graphs in the same order. This paper investigates the diagnosability on two categories of multigraph composition networks, that is, multigraph alternating composition networks and multigraph 2-matching composition networks, under the PMC and MM* models. As corollaries, the diagnosability of several known networks, such as alternating group graphs, k-ary n-cubes, and round matching composition networks, can be derived directly.
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收藏
页数：11
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