A spectral approach to learning structural variations in graphs

  • Authors:
  • Bin Luo;Richard C. Wilson;Edwin R. Hancock

  • Affiliations:
  • Department of Computer Science, University of York, Heslington, York, YO10 5DD, UK;Department of Computer Science, University of York, Heslington, York, YO10 5DD, UK;Department of Computer Science, University of York, Heslington, York, YO10 5DD, UK

  • Venue:
  • Pattern Recognition
  • Year:
  • 2006

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Abstract

This paper shows how to construct a linear deformable model for graph structure by performing principal components analysis (PCA) on the vectorised adjacency matrix. We commence by using correspondence information to place the nodes of each of a set of graphs in a standard reference order. Using the correspondences order, we convert the adjacency matrices to long-vectors and compute the long-vector covariance matrix. By projecting the vectorised adjacency matrices onto the leading eigenvectors of the covariance matrix, we embed the graphs in a pattern-space. We illustrate the utility of the resulting method for shape-analysis.