A Parallel Improvement Algorithm for the Bipartite Subgraph Problem

Kuo Chun Lee, Nobuo Funabiki, Yoshiyasu Takefuji

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)


Since McCulloch and Pitts proposed an artificial neuron model in 1943, several neuron models have been investigated. This paper proposes the first parallel improvement algorithm using the maximum neural network model for the bipartite subgraph problem. The goal of this NP-complete problem is to remove the minimum number of edges in a given graph such that the remaining graph is a bipartite graph. A large number of instances have been simulated to verify the proposed algorithm, with the simulation result showing that our algorithm finds a solution within 200 iteration steps and the solution quality is superior to that of the best existing algorithm. The algorithm is extended for the k-partite subgraph problem, where no algorithm has been proposed.

Original languageEnglish
Pages (from-to)139-145
Number of pages7
JournalIEEE Transactions on Neural Networks
Issue number1
Publication statusPublished - 1992 Jan
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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