Homology-Preserving Multi-Scale Graph Skeletonization Using Mapper on Graphs

Code

• github.com/shape-vis/mapperongraphs
  OfficialIn papernone★ 0
• github.com/USFDataVisualization/MapperOnGraphs
  OfficialIn papernone★ 0
• github.com/crisbodnar/dgm
  pytorch★ 58

Abstract

Node-link diagrams are a popular method for representing graphs that capture relationships between individuals, businesses, proteins, and telecommunication endpoints. However, node-link diagrams may fail to convey insights regarding graph structures, even for moderately sized data of a few hundred nodes, due to visual clutter. We propose to apply the mapper construction -- a popular tool in topological data analysis -- to graph visualization, which provides a strong theoretical basis for summarizing the data while preserving their core structures. We develop a variation of the mapper construction targeting weighted, undirected graphs, called , which generates homology-preserving skeletons of graphs. We further show how the adjustment of a single parameter enables multi-scale skeletonization of the input graph. We provide a software tool that enables interactive explorations of such skeletons and demonstrate the effectiveness of our method for synthetic and real-world data.

Tasks

Reproductions