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DOI: 10.4230/LIPIcs.GD.2025.56

Using Reinforcement Learning to Optimize the Global and Local Crossing Number (Poster Abstract)

Authors: Timo Brand, Henry Förster, Stephen Kobourov, Robin Schukrafft, Markus Wallinger, and Johannes Zink

Published in: LIPIcs, Volume 357, 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)

Abstract

We present a novel approach to graph drawing based on reinforcement learning for minimizing the global and the local crossing number, that is, the total number of edge crossings and the maximum number of crossings on any edge, respectively. An agent learns how to move a vertex based on a given observation vector. The agent receives feedback in the form of local reward signals tied to crossing reduction. To generate an initial layout, we use a stress-based graph-drawing algorithm. We compare our method against force- and stress-based baseline algorithms as well as three established algorithms for global crossing minimization on a suite of benchmark graphs. The experiments show mixed results: our current algorithm is mainly competitive for the local crossing number.

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Timo Brand, Henry Förster, Stephen Kobourov, Robin Schukrafft, Markus Wallinger, and Johannes Zink. Using Reinforcement Learning to Optimize the Global and Local Crossing Number (Poster Abstract). In 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 357, pp. 56:1-56:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{brand_et_al:LIPIcs.GD.2025.56,
  author =	{Brand, Timo and F\"{o}rster, Henry and Kobourov, Stephen and Schukrafft, Robin and Wallinger, Markus and Zink, Johannes},
  title =	{{Using Reinforcement Learning to Optimize the Global and Local Crossing Number}},
  booktitle =	{33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)},
  pages =	{56:1--56:4},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-403-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{357},
  editor =	{Dujmovi\'{c}, Vida and Montecchiani, Fabrizio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GD.2025.56},
  URN =		{urn:nbn:de:0030-drops-250420},
  doi =		{10.4230/LIPIcs.GD.2025.56},
  annote =	{Keywords: Reinforcement Learning, Crossing Minimization, Local Crossing Number}
}

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Using Reinforcement Learning to Optimize the Global and Local Crossing Number (Poster Abstract)

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