DocumentCode
816196
Title
An Improved Neural Network Model for the Two-Page Crossing Number Problem
Author
Hongmei He ; Sykora, O. ; Makinen, E.
Author_Institution
Dept. of Comput. Sci., Loughborough Univ.
Volume
17
Issue
6
fYear
2006
Firstpage
1642
Lastpage
1646
Abstract
The simplest graph drawing method is that of putting the vertices of a graph on a line and drawing the edges as half-circles either above or below the line. Such drawings are called two-page book drawings. The smallest number of crossings over all two-page drawings of a graph G is called the two-page crossing number of G. Cimikowski and Shope have solved the two-page crossing number problem for an n-vertex and m-edge graph by using a Hopfield network with 2 m neurons. We present here an improved Hopfield model with m neurons. The new model achieves much better performance in the quality of solutions and is more efficient than the model of Cimikowski and Shope for all graphs tested. The parallel time complexity of the algorithm, without considering the crossing number calculations, is O(m) for the new Hopfield model with m processors clearly outperforming the previous algorithm
Keywords
Hopfield neural nets; computational complexity; graph theory; Hopfield network; improved neural networks; learning algorithm; parallel time complexity; simplest graph drawing method; two-page crossing number problem; Books; Costs; Equations; Large scale integration; Neural networks; Neurons; Semiconductor device modeling; Testing; Very large scale integration; Energy function; Hopfield model; learning algorithm; motion equation; two-page crossing number; Algorithms; Information Storage and Retrieval; Neural Networks (Computer); Pattern Recognition, Automated;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/TNN.2006.881486
Filename
4012025
Link To Document