A Binary Neural Network Algorithm for the Graph Partitioning Problem

Yasuhiro Tamaki, Nobuo Funabiki, Seishi Nishikawa

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


The graph partitioning problem is an NP hard problem of deriving the partitioning of each vertex such that the total stun of the edge weights among the groups is minimized and the total sum of the vertex weights in each group is less than the upper limit. In this paper, a neural network solution is proposed in which the binary neurons are used for the graph 2-partitioning problem. In the present neural network, an energy function that is applicable to graphs both with and without edge and vertex weights is defined. For improvement of solution accuracy, shaking terms are introduced into the operating equation. To evaluate the solution search capability of the present method, simulations are carried out for random graphs, together with the KL method proposed by Kernighan and Lin, and the FM method proposed by Fiduccia and Mattheyses. From the simulation results, it is shown that the solutions obtained by the present method are the best.

Original languageEnglish
Pages (from-to)34-42
Number of pages9
JournalElectronics and Communications in Japan, Part III: Fundamental Electronic Science (English translation of Denshi Tsushin Gakkai Ronbunshi)
Issue number12
Publication statusPublished - Dec 1999
Externally publishedYes


  • FM method
  • Graph partitioning problem
  • KL method
  • NP hard
  • Neural network
  • Operating equation
  • Shaking term

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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