Title
Two pages graph layout via recurrent multivalued neural networks
Abstract
In this work, we propose the use of two neural models performing jointly in order to minimize the same energy function. This model is focused on obtaining good solutions for the two pages book crossing problem, although some others problems can be efficiently solved by the same model. The neural technique applied to this problem allows to reduce the energy function by changing outputs from both networks -outputs of first network representing location of nodes in the nodes line, while the outputs of the second one meaning the half-plane where the edges are drawn. Detailed description of the model is presented, and the technique to minimize an energy function is fully described. It has proved to be a very competitive and efficient algorithm, in terms of quality of solutions and computational time, when compared to the state-of-the-art methods. Some simulation results are presented in this paper, to show the comparative efficiency of the methods.
Year
DOI
Venue
2007
10.1007/978-3-540-73007-1_24
IWANN
Keywords
Field
DocType
energy function,good solution,pages graph layout,pages book,computational time,recurrent multivalued neural network,detailed description,nodes line,neural model,efficient algorithm,comparative efficiency,neural technique,neural network
Computer science,Theoretical computer science,Artificial intelligence,Artificial neural network,Machine learning,Graph Layout
Conference
Volume
ISSN
Citations 
4507
0302-9743
0
PageRank 
References 
Authors
0.34
12
4