DimFlux: Force-Directed Doubly-Additive Line Diagrams

This repository implements a force-directed placement approach for doubly-additive line drawings.

References

This project is based on the theory and methods described in the following works.

Primary Reference

@article{Noehre2026,
  title = {DimFlux: Force-directed additive line diagrams},
  journal = {International Journal of Approximate Reasoning},
  volume = {197},
  pages = {109734},
  year = {2026},
  issn = {0888-613X},
  doi = {https://doi.org/10.1016/j.ijar.2026.109734},
  url = {https://www.sciencedirect.com/science/article/pii/S0888613X2600109X},
  author = {Marcel Nöhre and Dominik Dürrschnabel and Bernhard Ganter and Gerd Stumme},
  keywords = {Formal concept analysis, Force-directed placement, Additive line diagrams, DimDraw, DimFlux},
  abstract = {The visualization of concept lattices is a central problem in the field of Formal Concept Analysis. Force-directed algorithms, as popular in graph drawing, are a promising approach, treating lattice diagrams as physical models, optimizing node positions based on forces derived from the lattice structure. We build on the work of Zschalig, who, however, limited himself to attribute-additive diagrams. We use a more general additivity, in which both the attributes and the objects contribute to the positions of the concept nodes. We replace the planarity enhancer used by Zschalig to obtain a starting diagram for force-directed optimization with the DimDraw algorithm, which generates structured order diagrams on its own. The combination results in DimFlux, an algorithm that leverages the advantages of DimDraw but generates additive diagrams in which readability is increased by maximizing the conflict distance between nodes and non-incident edges.}
}

Supplementary Material

@misc{Noehre2026Supplementary,
  author = {Nöhre, Marcel and Dürrschnabel, Dominik and Ganter, Bernhard and Stumme, Gerd},
  title = {A Visual Benchmark of DimFlux: Comparison of Line Diagrams for Concept Lattices},
  month = mar,
  year = 2026,
  publisher = {Zenodo},
  doi = {10.5281/zenodo.18936106},
  url = {https://doi.org/10.5281/zenodo.18936106}
}

If you use this code in your work, please cite the above references.

Usage

Install via PyPI

Install the package from PyPI using pip.

pip install dim-flux

Execute the layout generation using the dim-flux command.

dim-flux

Install from source

Clone the repository and initialize the environment. uv will automatically create a virtual environment and sync dependencies based on the pyproject.toml. uv will automatically create a virtual environment.

uv sync

Execute the layout generation using uv run to ensure the correct environment context.

uv run dim-flux

The script will prompt you for a path to a .cxt file. To export the node positions add the --export flag.

uv run dim-flux --export

The --export flag also works with the PyPI install:

dim-flux --export

Use as a library

DimFlux can also be called directly from Python via dim_flux.plot.

from dim_flux import plot

# cxt can be a path to a .cxt file, or a pandas DataFrame representing the incidence matrix
positions = plot('path/to/context.cxt')

By default plot returns the computed (x, y) coordinates in lectic order as a numpy array. Pass export=True to instead write the PDF, GraphML and .pos exports to disk.

plot('path/to/context.cxt', export=True)

Configuration

License

Shield:

This work is licensed under a Creative Commons Attribution 4.0 International License.