More faithfulness graph embedding

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Using dimensionality reduction idea to visualize graph data sets can preserve the properties of the original space and reveal the underlying information shared among data points. Continuity Trustworthy Graph Embedding (CTGE) is new method we have introduced in this paper to improve the faithfulness of the graph visualization. We will use CTGE in graph field to find new understandable representation to be more easy to analyze and study. Several experiments on real graph data sets are applied to test the effectiveness and efficiency of the proposed method, which showed CTGE generates highly faithfulness graph representation when compared its representation with other methods.


  • Keywords


    Graph drawing; Information visualization and dimensionality reduction method.

  • References


      [1] D. Harel, Y. Koren, Graph drawing by high-dimensional embedding, Graph Algorithms and Applications 8 (2004) 195-214.

      [2] D. A. R. Bourqui, P. Mary, How to draw clustered weighted graphs using a multilevel force-directed graph drawing algorithm, in: In Proc. of the 11 Int. Conf. on Information Visualisation (IV'07), pages 757-764, Washington, USA., 2007.

      [3] J. P. JOSEP D'IAZ, M. SERNA, A survey of graph layout problems, ACM Computing Surveys 34 (2002) 313-356.

      [4] Y. Hu, E_cient, high-quality force-directed graph drawing, The Mathematica Journal 10 (2006) 37-71.

      [5] P. Eades, M. Huang, Navigating clustered graphs using force-directed methods, Graph Algorithms and Applications 4 (2000) 183-210.

      [6] M. G. P. Gajer, S. Kobourov, A multi-dimensional approach to forcedirected layouts of large graphs, Computational Geometry: Theory and Applications 29 (2004) 3-18.

      [7] K. M. Tim Dwyer, M. Wybrow, Integrating edge routing into force-directed layout, Lecture Notes in Computer Science 4372 (2007) 8-19.

      [8] Y. Koren, On spectral graph drawing, COCOON (2003) 496-508.

      [9] I. T. Jolli_e, Principal Component Analysis, New York: Springer-Verlag, 2002.

      [10] D. Harel, Y. Koren, Graph drawing by high-dimensional embedding, GraphDrawing (2002) 207-219.

      [11] T. B. H. Albert D. Shieh, E. M. Airoldi, Tree preserving embedding, in: Proceedings of the 28th InternationalConference on Machine Learning, Bellevue, WA, USA, 2011.

      [12] B. H. Junker, F. Schreiber (Eds.), Analysis of Biological Networks, wiley & Sons Inc., 2008.

      [13] V. V. Onclinx, M.Verleysen, Nonlinear data projection on non-euclidean manifolds with controlled trade-o_ between trustworthiness and continuity, Neurocomputing 72 (2009) 1444-1454.

      [14] D. K. Agra_otis, Stochastic proximity embedding, Computational Chemistry 24.

      [15] J. T. V. De Silva, Global versus local methods in nonlinear dimensionality reduction, Advances in Neural Information Processing Systems 15 (2003) 705-712.

      [16] M. O. J. V. P. T. Samuel Kaski, Janne Nikkil, E. Castrn, Trustworthiness and metrics in visualizing similarity of gene expression, BMC Bioinformatics 4: 48 (2003) (2003) 4:48.

      [17] J. Venna, S. Kaski, Comparison of visualization methods for an atlas of gene expression data sets, Information Visualization 6 (2007) 139-154.

      [18] M. Mignotte, A bicriteria optimization approach based dimensionality reduction model for the color display of Hyperspectal images, IEEE Transactions on Geoscience and Remote Sensing 50 (2012) 501-513.

      [19] L. Chen, A. Buja, Local multidimensional scaling for nonlinear dimension reduction, graph drawing, andproximity analysis, Journal of the American Statistical Association 104 (2009) 209-219.


 

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Article ID: 4419
 
DOI: 10.14419/ijamr.v4i2.4419




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