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Projection of undirected and non-positional graphs using self organizing maps

Conference Paper


Abstract


  • Kohonen's Self-Organizing Map is a popular method which allows the projection of high dimensional data onto a low dimensional display space. Models of Self-Organizing Maps for the treatment of graphs have also been defined and studied. This paper proposes an extension to the GraphSOM model which substantially improves the stability of the model, and, as a side effect, allows for an acceleration of training. The proposed extension is based on a soft encoding of the information needed to represent the vertices of an input graph. Experimental results demonstrate the advantages of the proposed extension.

Publication Date


  • 2009

Citation


  • Hagenbuchner, M., Zhang, S. J., Tsoi, A. C., & Sperduti, A. (2009). Projection of undirected and non-positional graphs using self organizing maps. In ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (pp. 559-564).

Scopus Eid


  • 2-s2.0-84886994902

Web Of Science Accession Number


Start Page


  • 559

End Page


  • 564

Abstract


  • Kohonen's Self-Organizing Map is a popular method which allows the projection of high dimensional data onto a low dimensional display space. Models of Self-Organizing Maps for the treatment of graphs have also been defined and studied. This paper proposes an extension to the GraphSOM model which substantially improves the stability of the model, and, as a side effect, allows for an acceleration of training. The proposed extension is based on a soft encoding of the information needed to represent the vertices of an input graph. Experimental results demonstrate the advantages of the proposed extension.

Publication Date


  • 2009

Citation


  • Hagenbuchner, M., Zhang, S. J., Tsoi, A. C., & Sperduti, A. (2009). Projection of undirected and non-positional graphs using self organizing maps. In ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (pp. 559-564).

Scopus Eid


  • 2-s2.0-84886994902

Web Of Science Accession Number


Start Page


  • 559

End Page


  • 564