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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
The Computer Journal
Article . 2025 . Peer-reviewed
License: OUP Standard Publication Reuse
Data sources: Crossref
DBLP
Article . 2025
Data sources: DBLP
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Cyclic connectivity and cyclic diagnosability of alternating group graphs

Authors: Ting Tian; Shumin Zhang; He Li;

Cyclic connectivity and cyclic diagnosability of alternating group graphs

Abstract

Abstract The generalizations of the connectivity and diagnosability are significant parameters to evaluate the reliability and the fault-tolerance of multiprocessor systems, and play an important role in designing and maintaining multiprocessor systems. In order to better measure the reliability of a system, some scholars proposed the cyclic connectivity and cyclic diagnosability, which request that there are at least two components containing cycles after removing the vertex set. Interconnection networks are typically used as the underlying topologies of a multiprocessor system. In particular, alternating group graphs possess many attractive properties, such as vertex transitivity, strong hierarchy, and maximal connectivity, making them excellent choices for interconnection networks in a multiprocessor system. In this paper, we determine that the cyclic connectivity of the alternating group graph is $6n-18$ for $n\ge 4$. Moreover, we establish that its cyclic diagnosability is $8n-24$ for $n>6$ under the $PMC$ model and $MM^{*}$ model, respectively.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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