Presentation 1996/12/14
A Binary Neural Network Algorithm for the Graph Partitioning Problem
Yasuhiro Tamaki, Nobuo Funabiki, Seishi Nishikawa,
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Abstract(in English) In this paper, we present a neural network algorithm using binary neurons for a graph two-partitioning problem. For a given graph G(V, E), the goal of this NP-hard problem is to find a partition of vertices in V into two groups such that not only the sum of vertex-weights in each group is within the upper bound, but also the sum of edge-weights between groups is minimized. We formulate a new energy function and introduce a shaking term in the motion equation in order to improve the solution quality. We compare the performance of the neural network with two existing typical algorithms through simulations in unweighted and weighted random graphs. The results show that the neural network is comparable with existing algorithms for vertex-unweighted graphs in terms of the solution quality, and that it is much better for vertex-weighted graphs.
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Keyword(in English) neural network / graph two-partitioning problem / NP hard problem / shaking term
Paper # NC96-64
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Committee NC
Conference Date 1996/12/14(1days)
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Paper Information
Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Binary Neural Network Algorithm for the Graph Partitioning Problem
Sub Title (in English)
Keyword(1) neural network
Keyword(2) graph two-partitioning problem
Keyword(3) NP hard problem
Keyword(4) shaking term
1st Author's Name Yasuhiro Tamaki
1st Author's Affiliation Division of Informatics and Mathematical Science, Graduate School of Engineering Science, Osaka University()
2nd Author's Name Nobuo Funabiki
2nd Author's Affiliation Division of Informatics and Mathematical Science, Graduate School of Engineering Science, Osaka University
3rd Author's Name Seishi Nishikawa
3rd Author's Affiliation Division of Informatics and Mathematical Science, Graduate School of Engineering Science, Osaka University
Date 1996/12/14
Paper # NC96-64
Volume (vol) vol.96
Number (no) 430
Page pp.pp.-
#Pages 8
Date of Issue