A visualization method for metagraphs with complex multi-level hierarchical structures
- Authors: Molchanov A.V.1, Gapanyuk Y.E.1, Afanasyev G.I.1
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Affiliations:
- Bauman Moscow State Technical University
- Issue: Vol 12, No 3 (2025)
- Pages: 58-66
- Section: SYSTEM ANALYSIS, INFORMATION MANAGEMENT AND PROCESSING, STATISTICS
- URL: https://journals.eco-vector.com/2313-223X/article/view/695660
- DOI: https://doi.org/10.33693/2313-223X-2025-12-3-58-66
- EDN: https://elibrary.ru/BAJDCN
- ID: 695660
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Abstract
Graph visualization is a crucial tool for the visual analysis of interconnected data; however, traditional methods often fail to efficiently represent nested, multi-level hierarchical structures. This study proposes a method based on classical force-directed layout algorithms, specifically designed for visualizing metagraphs through a planetary interaction model of vertices. The distinctive feature of the proposed approach lies in incorporating the intrinsic weight of each vertex. This adaptation allows for the preservation of nested structures, improved graph readability, and scalability of the visualization as data complexity increases. Simulation results demonstrate the method's effectiveness across various types of nested data, including file systems, organizational hierarchies, and biological ontologies.
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About the authors
Aleksei V. Molchanov
Bauman Moscow State Technical University
Author for correspondence.
Email: molchanovav@student.bmstu.ru
SPIN-code: 5436-5405
postgraduate student
Russian Federation, MoscowYuriy E. Gapanyuk
Bauman Moscow State Technical University
Email: gapyu@bmstu.ru
SPIN-code: 4758-2148
Cand. Sci. (Eng.), Associate Professor
Russian Federation, MoscowGennady I. Afanasyev
Bauman Moscow State Technical University
Email: gaipcs@bmstu.ru
SPIN-code: 7790-1645
Cand. Sci. (Eng.), Associate Professor
Russian Federation, MoscowReferences
- Antonov E.V., Artamonov A.A., Rudik A.V., Malugin M.I. Visualization of trends in the scientific field: A proposed method and a review of big data. Scientific Visualization. 2022. Vol. 14. No. 2. Pp. 62–76. (In Rus.). doi: 10.26583/sv.14.2.06.
- Gorshkov Yu.G. Visualization of lung sounds based on multilevel wavelet analysis. Scientific Visualization. 2022. Vol. 14. No. 2. Pp. 18–26. (In Rus.). doi: 10.26583/sv.14.2.02.
- Gapanyuk Yu.E. Stages of development of the metagraph model of data and knowledge. In: Integrated Models and Soft Computing in Artificial Intelligence (IMI-2021). Collection of Scientific papers of the X International Scientific and Technical conf. In 2 vols. Vol. 2. 2021. Pp. 190–200.
- Gureev V., Mazov N. Increasing the role of open bibliographic data in conditions of limited access to proprietary IP. Theory and Practice of Science Management. 2023. No. 5. Pp. 49–76. (In Rus.). doi: 10.19181/smtp.2023.5.2.4.
- Karpachevskij A. Theoretical and technological problems of geographical network analysis. In: Geodesy, cartography, geoinformatics, and cadastre. Innovations in science, education and production. 2024. doi: 10.52565/9785911553449.
- Kasyanov V.N. Methods and means of information visualization based on attributed hierarchical graphs with ports. Siberian Aerospace Journal. 2023. Vol. 24. No. 1. Pp. 8–17. (In Rus.). doi: 10.31772/2712-8970-2023-24-1-8-17.
- Isaev R.A., Podesovsky A.G., Zakharova A.A. Metaphors of visualization in the tasks of exploration analysis of heterogeneous data. Scientific Visualization. 2024. Vol. 16. No. 5. Pp. 56–74. (In Rus.). doi: 10.26583/sv.16.5.04.
- Omelyanchuk N.A., Rybakov M.A., Zemlyanskaya E.V. Methods of reconstruction of gene regulatory networks based on transcriptomic data of individual cells. Vavilovsky Journal of Genetics and Breeding. 2024. Vol. 28. No. 8. Pp. 974–981. (In Rus.)
- Terekhov V.I., Chernenkiy V.M., Gapanyuk Yu.E. Representation of complex networks based on metagraphs. In: Neuroinformatics–2016. Collection of scientific papers of the XVIII All-Russian scientific and technical conf. In 3 parts. Part 1. Moscow: National Research Nuclear University MEPhI, 2016. Pp. 225–235.
- Chatzimparmpas A., Kucher K., Kerren A. Visualization for trust in machine learning revisited: The state of the field in 2023. IEEE Computer Graphics and Applications. 2024. Vol. 44. No. 3. Pp. 99–113. doi: 10.1109/MCG.2024.3360881.
- Davidson R., Harel D. Drawing graphs nicely using simulated annealing. ACM Transactions on Graphics. 1996. Vol. 15. Pp. 301–331.
- Jacomy M., Venturini T., Heymann S., Bastian M. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the gephi software. PLoS ONE. 2014. Pp. 1–12.
- Jeong H., Tombor B., Albert R. et al. The large-scale organization of metabolic networks. Nature. 2000. Vol. 407. Pp. 651–654. doi: 10.1038/35036627.
- Noack A. An energy model for visual graph clustering. In: Graph drawing. G. Liotta (ed.). Berlin; Heidelberg: Springer, 2003. Vol. 2912. Pp. 425–436. (Lecture Notes in Computer Science).
- Noack A. Unified quality measures for clusterings, layouts, and orderings of graphs, and their application as software design criteria. Dis. … of PhD. Cottbus: Brandenburg University of Technology, 2007. 303 p.
- Regan E., Barabási A.-L. Hierarchical organization in complex networks // Physical Review E. 2003. Vol. 67. doi: 10.1103/PhysRevE.67.026112.
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