Embedding, clustering and coloring for dynamic maps

Yifan Hu, Stephen G. Kobourov, Sankar Veeramoni

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


We describe a practical approach for visualizing multiple relationships defined on the same dataset using a geographic map metaphor, where clusters of nodes form countries and neighboring countries correspond to nearby clusters. Our aim is to provide a visualization that allows us to compare two or more such maps (showing an evolving dynamic process, or obtained using different relationships). In the case where we are considering multiple relationships, e.g., different similarity metrics, we also provide an interactive tool to visually explore the effect of combining two or more such relationships. Our method ensures good readability and mental map preservation, based on dynamic node placement with node stability, dynamic clustering with cluster stability, and dynamic coloring with color stability.

Original languageEnglish (US)
Pages (from-to)77-109
Number of pages33
JournalJournal of Graph Algorithms and Applications
Issue number1
StatePublished - 2014

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science
  • Computer Science Applications
  • Geometry and Topology
  • Computational Theory and Mathematics


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