Conditional edge-fault hamiltonicity of matching composition networks

Sun Yuan Hsieh, Chia Wei Lee

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)


A graph G is called Hamiltonian if there is a Hamiltonian cycle in G. The conditional edge-fault Hamiltonicity of a Hamiltonian graph G is the largest k such that after removing k faulty edges from G, provided that each node is incident to at least two fault-free edges, the resulting graph contains a Hamiltonian cycle. In this paper, we sketch common properties of a class of networks, called Matching Composition Networks (MCNs), such that the conditional edge-fault Hamiltonicity of MCNs can be determined from the found properties. We then apply our technical theorems to determine conditional edge-fault Hamiltonicities of several multiprocessor systems, including n-dimensional crossed cubes, n-dimensional twisted cubes, n-dimensional locally twisted cubes, n-dimensional generalized twisted cubes, and n-dimensional hyper Petersen networks. Moreover, we also demonstrate that our technical theorems can be applied to network construction.

Original languageEnglish
Pages (from-to)581-592
Number of pages12
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number4
Publication statusPublished - 2009

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics


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