https://doi.org/10.1140/epjds/s13688-025-00615-0
Research
Layered community-aware layout method for complex statute network visualization
1
Graduate School of Culture Technology, Korea Advanced Institute of Science and Technology (KAIST), 34141, Daejeon, Republic of Korea
2
Korea Institute for Advanced Study (KIAS), 02455, Seoul, Republic of Korea
3
Lawfirm Garosu, 5 Garosugil, Gangnam-gu, 06029, Seoul, Republic of Korea
Received:
5
June
2025
Accepted:
17
December
2025
Published online:
22
December
2025
A country’s statutes (laws) are the foundation of its legal system. The statutes are connected to each other through citations, forming a complex, hierarchical network. While there have been efforts to analyze the network properties of the statute networks of some countries, the fact that a country’s law touches every aspect of the residents of a country suggests that there further exist multiple worthy avenues to pursue from complex network and data science. Developments in those areas can be of help not only to the legal professionals but also to the general public in understanding and making use of the laws. One such avenue would be an effective visualization technique for the statute network that reveals its structural features for practical exploration and navigability. Here, we propose the Layered Community-Aware Layout (LCGraph) visualization technique. LCGraph detects densely connected communities through recursive clustering and then minimizes community overlaps and edge crossings to reveal the network’s structure. We apply our method to the complete statute network of the Republic of Korea, then compare it with a number of other methods through a set of quantitative parameters and a user study. We show that LCGraph outperforms the others by achieving an effective balance between aesthetic clarity, community cohesion, and the preservation of both local and global structures, indicating suitability for statutes from many jurisdictions.
Key words: Network analysis / Community-aware layout / Network of statutes / Layered visualization
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-025-00615-0.
© The Author(s) 2025
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
