Component diagnosability in terms of component connectivity of hypercube-based compound networks

Jiafei Liu, Shuming Zhou, Dajin Wang, Hong Zhang

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Enhancing the invulnerability of multiprocessor systems against malicious attacks has been regarded as one of the important issues in network science and big data era. Thus, in order to firmly characterize the robustness of systems, several variants of classic connectivity have been proposed so far. The component connectivity is a significant metric in evaluating the robustness and fault tolerability of interconnection network. For an interconnection network G and a positive integer h, the (h+1)-component connectivity of G, denoted cκh+1(G), is the cardinality of a minimum vertex cut F such that G−F has at least h+1 connected components. Based on component connectivity, component diagnosability has been proposed to measure the self-diagnosis capability of multiprocessor systems. In this paper, we suggest some characterizations of the (h+1)-component connectivity of a class of regular networks under some restrictions. Furthermore, we establish the relationship between component connectivity and component diagnosability of one class of networks. As by-products, we present the (h+1)-component diagnosability of the state-of-the-art compound networks based on hypercube, such as bicube network, generalized exchanged hypercube, hierarchical hypercube, half-hypercube, and so on.

Original languageEnglish
Pages (from-to)17-26
Number of pages10
JournalJournal of Parallel and Distributed Computing
StatePublished - Apr 2022


  • Component connectivity
  • Component diagnosability
  • Multiprocessor systems
  • Robustness


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