A neural network model for finding a near-maximum clique

Nubuo Funabiki, Yoshiyasu Takefuji, Kuo Chun Lee

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

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
Volume14
Issue number3
DOIs
Publication statusPublished - 1992
Externally publishedYes

Fingerprint

Maximum Clique
Neural Network Model
Neural networks
Parallel algorithms
Parallel Algorithms
Graph in graph theory
Maximum Clique Problem
Parallel Machines
Time Constant
Subgraph
Adjacent
NP-complete problem
Vertex of a graph
Processing
Simulation

ASJC Scopus subject areas

  • Computer Science Applications
  • Hardware and Architecture
  • Control and Systems Engineering

Cite this

A neural network model for finding a near-maximum clique. / Funabiki, Nubuo; Takefuji, Yoshiyasu; Lee, Kuo Chun.

In: Journal of Parallel and Distributed Computing, Vol. 14, No. 3, 1992, p. 340-344.

Research output: Contribution to journalArticle

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