A systematic survey on complex network visualization in industrial manufacturing and production
This paper presents a systematic survey of the visualization and visual analysis of complex networks as generated in studies on industrial manufacturing and production problems. We provide a description and categorization of the types of networks found in this area. We also investigate the concepts used for revealing structural properties needed to explore information relevant to industrial manufacturing and production. We discuss the requirements for visualizing complex networks in this domain so as to achieve a meaningful representation of industrial knowledge for different users. Additionally, we focus on immersive visualization for networks, due to its growing importance in recent years, as well as the wide range of opportunities that come with it. We highlight the distinct challenges associated with this area, which are primarily related to rendering, navigation, and interaction along the reality-virtuality continuum. Finally, a categorization into three types of layout algorithms is presented, allowing for intuitive and systematic visualization of complex and large networks. This provides insights beyond mere node and edge visualization by including key topological characteristics, measures, and comparative analysis methods. These algorithms enable fast optimization of the coordinates of the network vertices. This paper thus discusses the types of networks found in industrial manufacturing and production, their construction, as well as the visual exploration of respective networks, in order to explore such networks in terms of their structural properties using conventional and immersive visualization techniques.
Authors
- Bernhard Fröhler (ORCID: https://orcid.org/0000-0003-1271-0838)
- Herbert Jodlbauer (ORCID: https://orcid.org/0000-0002-0373-6625)
- Christoph Heinzl (ORCID: https://orcid.org/0000-0002-3173-8871)
- Shailesh Tripathi (ORCID: https://orcid.org/0000-0002-3484-6368)
Institutions
- University of Passau (DE)
- Fraunhofer Institute for Integrated Circuits (DE)
- University of Applied Sciences Upper Austria (AT)
Publication Details
- Journal
- Applied Network Science
- Published
- 2026-09-25
- DOI
- https://doi.org/10.1007/s41109-026-00834-y
- Primary Topic
- Data Visualization and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00