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Homology-Preserving Multi-Scale Graph Skeletonization Using Mapper on Graphs (1804.11242v5)

Published 3 Apr 2018 in cs.SI and stat.ML

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 {\mog}, 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.

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Authors (3)
  1. Paul Rosen (41 papers)
  2. Mustafa Hajij (51 papers)
  3. Bei Wang (102 papers)
Citations (5)
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