A Heuristic Approach towards Drawings of Graphs with High Crossing Resolution
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Abstract (arXiv)
The crossing resolution of a non-planar drawing of a graph is the value of the minimum angle formed by any pair of crossing edges. Recent experiments have shown that the larger the crossing resolution is, the easier it is to read and interpret a drawing of a graph. However, maximizing the crossing resolution turns out to be an NP-hard problem in general and only heuristic algorithms are known that are mainly based on appropriately adjusting force-directed algorithms. In this paper, we propose a new heuristic algorithm for the crossing resolution maximization problem and we experimentally compare it against the known approaches from the literature. Our experimental evaluation indicates that the new heuristic produces drawings with better crossing resolution, but this comes at the cost of slightly higher aspect ratio, especially when the input graph is large.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Aug 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2021Graph Neural Networks for Graph Drawingcited by 8
- 2022SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goalscited by 6
- 2025Using Reinforcement Learning to Optimize the Global and Local Crossing Numbercited by 0
- 2026Beauty in the Eye of AI: Aligning LLMs and Vision Models with Human Aesthetics in Network Visualizationcited by 0
- 2026Beauty in the Eye of AI: Aligning LLMs and Vision Models with Human Aesthetics in Network Visualization3 Apr 2026
- 2025Using Reinforcement Learning to Optimize the Global and Local Crossing Number7 Sep 2025
- 2022SmartGD: A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals13 Jun 2022
- 2021Graph Neural Networks for Graph Drawing21 Sep 2021