Geodesic obstacle representation of graphs

Prosenjit Bose, Paz Carmi, Vida Dujmović, Saeed Mehrabi, Fabrizio Montecchiani, Pat Morin, Luís Fernando Schultz Xavier da Silveira

Research output: Contribution to journalArticlepeer-review


An obstacle representation of a graph is a mapping of the vertices onto points in the plane and a set of connected regions of the plane (called obstacles) such that the straight-line segment connecting the points corresponding to two vertices does not intersect any obstacles if and only if the vertices are adjacent in the graph. Recently, Biedl and Mehrabi (Graph Drawing 2017) studied non-blocking grid obstacle representations of graphs in which the vertices of the graph are mapped onto points in the plane while the straight-line segments representing the adjacency between the vertices are replaced by the L1 (Manhattan) shortest paths in the plane that avoid obstacles. In this paper, we introduce the notion of geodesic obstacle representations of graphs with the main goal of providing a generalized model, which comes naturally when viewing line segments as shortest paths in the Euclidean plane. To this end, we extend the definition of obstacle representation by allowing some obstacles-avoiding shortest path between the corresponding points in the underlying metric space whenever the vertices are adjacent in the graph. We consider both general and plane variants of geodesic obstacle representations (in a similar sense to obstacle representations) under any polyhedral distance function in Rd as well as shortest path distances in graphs. Our results generalize and unify the notions of obstacle representations, plane obstacle representations and grid obstacle representations, leading to a number of questions on such representations.

Original languageEnglish
Article number101946
JournalComputational Geometry: Theory and Applications
StatePublished - 1 Feb 2023


  • Geodesic obstacle representation
  • Grid obstacle representation
  • Obstacle representation

ASJC Scopus subject areas

  • Computer Science Applications
  • Geometry and Topology
  • Control and Optimization
  • Computational Theory and Mathematics
  • Computational Mathematics


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