3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A Quality Metric for Symmetric Graph Drawings

      Preprint
      , , ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Symmetry is an important aesthetic criteria in graph drawing and network visualisation. Symmetric graph drawings aim to faithfully represent automorphisms of graphs as geometric symmetries in a drawing. In this paper, we design and implement a framework for quality metrics that measure symmetry, that is, how faithfully a drawing of a graph displays automorphisms as geometric symmetries. The quality metrics are based on geometry (i.e. Euclidean distance) as well as mathematical group theory (i.e. orbits of automorphisms). More specifically, we define two varieties of symmetry quality metrics: (1) for displaying a single automorphism as a symmetry (axial or rotational) and (2) for displaying a group of automorphisms (cyclic or dihedral). We also present algorithms to compute the symmetric quality metrics in O(n log n) time for rotational symmetry and axial symmetry. We validate our symmetry quality metrics using deformation experiments. We then use the metrics to evaluate a number of established graph drawing layouts to compare how faithfully they display automorphisms of a graph as geometric symmetries.

          Related collections

          Most cited references20

          • Record: found
          • Abstract: not found
          • Book Chapter: not found

          Graph Drawing by Stress Majorization

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Continuous Symmetry Measures. 4. Chirality

              Bookmark
              • Record: found
              • Abstract: not found
              • Book Chapter: not found

              Eigensolver Methods for Progressive Multidimensional Scaling of Large Data

                Bookmark

                Author and article information

                Journal
                11 October 2019
                Article
                1910.04974
                164ec4aa-fc20-444e-986a-9c7d176d7786

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                cs.DS cs.CG

                Theoretical computer science,Data structures & Algorithms
                Theoretical computer science, Data structures & Algorithms

                Comments

                Comment on this article