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Dense and symmetric graph formulation and generation for wireless information networks

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Dense and symmetric graphs are useful for modeling fast information distribution in wireless information networks. In this paper, we focus on a specific family of dense and symmetric graphs, the Borel Cayley graphs. More specifically, we investigate the various parameters in the original formulation of Borel Cayley graphs defined in the matrix domain. By eliminating redundant parameters, we propose a new and simpler formulation of Borel Cayley graphs. This new formulation is defined in the integer domain and the group operation resembles the generation of pseudorandom numbers, hence the name pseudo-random formulation. Through the establishment of propositions and corollaries, we proved that certain parameters do not affect the diameter under a specific condition. This result provides a guideline in choosing appropriate generators and thus reducing the computation time in the search of good or bad generators. Using this new formulation, we also show that Borel Cayley graphs are isomorphic to a sub-class of Cayley graphs proposed by Dinneen. Finally, some guidelines for choosing generators to avoid disconnected graphs are also provided.

Original languageEnglish
Title of host publicationInternational Conference on Wireless Networks and Information Systems, WNIS 2009
Pages379-384
Number of pages6
DOIs
StatePublished - 2009
EventInternational Conference on Wireless Networks and Information Systems, WNIS 2009 - Shanghai, China
Duration: Dec 28 2009Dec 29 2009

Publication series

NameInternational Conference on Wireless Networks and Information Systems, WNIS 2009

Conference

ConferenceInternational Conference on Wireless Networks and Information Systems, WNIS 2009
Country/TerritoryChina
CityShanghai
Period12/28/0912/29/09

Keywords

  • Cayley graph
  • Information networks
  • Pseudo random graph

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