Drawing Graphs in Parallel Lines with Artificial Neural Networks

 

Abstract

COVID data are usually presented in a non-structured format and mainly focused on healthy issues (incidence, mortality, etc). At the same time, Governments have designed a set of measures to deal with the Pandemic. In addition, several institutions have studied the economical effects of the situation in each country. In this work, we combine these three data sources and illustrate how Formal Concept Analysis can become a useful tool to discover relationships among these three views of the situation: health, politics and economy. Our aim is to provide an implication-driven approach to discover knowledge behind the data.

Citation

Please, cite this work as:

[CL08] E. M. Casermeiro and D. López-Rodríguez. “Drawing Graphs in Parallel Lines with Artificial Neural Networks”. In: 8th International Conference on Hybrid Intelligent Systems (HIS 2008), September 10-12, 2008, Barcelona, Spain. Ed. by F. Xhafa, F. Herrera, A. Abraham, M. Köppen and J. M. Benítez. cited By 0; Conference of 8th International Conference on Hybrid Intelligent Systems, HIS 2008 ; Conference Date: 10 September 2008 Through 12 September 2008; Conference Code:73852. Barcelona: IEEE Computer Society, 2008, pp. 667-671. DOI: 10.1109/HIS.2008.89. URL: [https://www.scopus.com/inward/record.uri?eid=2-s2.0-55349109310&doi=10.1109

BibTeX

@InProceedings{Casermeiro2008,
     author = {Enrique Mérida Casermeiro and Domingo López-Rodríguez},
     booktitle = {8th International Conference on Hybrid Intelligent Systems {(HIS} 2008), September 10-12, 2008, Barcelona, Spain},
     title = {Drawing Graphs in Parallel Lines with Artificial Neural Networks},
     year = {2008},
     address = {Barcelona},
     editor = {Fatos Xhafa and Francisco Herrera and Ajith Abraham and Mario K{"o}ppen and José Manuel Benítez},
     note = {cited By 0; Conference of 8th International Conference on Hybrid Intelligent Systems, HIS 2008 ; Conference Date: 10 September 2008 Through 12 September 2008; Conference Code:73852},
     pages = {667–671},
     publisher = {{IEEE} Computer Society},
     abstract = {In this work, we propose the use of a multivalued recurrent neural network with the aim of graph drawing. Particularly, the problem of drawing a graph in two parallel lines with the minimum number of crossings between edges is studied, and a formulation for this problem is presented. The neural model MREM is used to solve this problem. This model has been successfully applied to other optimization problems. In this case, a slightly different version is used, in which the neuron state is represented by a two dimensional discrete vector, representing the nodes assigned to a given position in each of the parallel lines. Some experimental simulations have been carried out in order to compare the efficiency of the neural network with a heuristic approach designed to solve the problem at hand. These simulations confirm that our neural model outperforms the heuristic approach, obtaining a lower number of crossings on average. © 2008 IEEE.},
     art_number = {4626707},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     biburl = {https://dblp.org/rec/conf/his/Merida-CasermeiroL08.bib},
     document_type = {Conference Paper},
     doi = {10.1109/HIS.2008.89},
     journal = {Proceedings - 8th International Conference on Hybrid Intelligent Systems, HIS 2008},
     keywords = {Drawing (graphics); Graph theory; Heuristic methods; Image classification; Intelligent control; Intelligent systems; Problem solving; Recurrent neural networks; Vegetation, Artificial neural networks; Discrete vectors; Experimental simulations; Heuristic approaches; Neural models; Of graphs; Optimization problems; Parallel lines, Neural networks},
     source = {Scopus},
     sponsors = {IEEE Systems Man and Cybernetics Society; European Neural Network Society; International Fuzzy Systems Association; European Society for Fuzzy Logic and Technology},
     url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-55349109310&doi=10.1109%2fHIS.2008.89&partnerID=40&md5=d3db07ff7d48861d1a455b54dd5c20db},
}