A neural network model for finding a near-maximum clique

Nubuo Funabiki, Yoshiyasu Takefuji, Kuo Chun Lee

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


A parallel algorithm based on the neural network model for finding a near-maximum clique is presented in this paper. A maximum clique of a graph G is a maximum complete subgraph of G where any two vertices are adjacent. The problem of finding a maximum clique is NP-complete. The parallel algorithm requires n processing elements for an n-vertex graph problem. The algorithm is verified by solving 230 different graph problems. The simulation results show that our computation time on a Macintosh IIfx is shorter than that of two better known algorithms on a Cray 2 and an IBM 3090 while the solution quality is similar. The algorithm solves a near-maximum clique problem in nearly constant time on a parallel machine with n processors.

Original languageEnglish
Pages (from-to)340-344
Number of pages5
JournalJournal of Parallel and Distributed Computing
Issue number3
Publication statusPublished - 1992 Mar
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence


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