Graphviz:sparse transformation - chunhualiao/public-docs GitHub Wiki

graphviz

The current GraphViz dot implementation does not use the sparse transformation from Eiglsperger et al. (2005) for several likely reasons, based on project history, technical debt, and community discussions:

  1. Legacy Codebase and Technical Debt:
    The dot engine is a mature, complex C codebase with many interdependent components. The dummy node insertion and expansion logic is deeply integrated into the pipeline (layer assignment, crossing minimization, coordinate assignment, etc.). Replacing it with the sparse transformation would require a major refactor, touching core data structures and algorithms.

  2. Backward Compatibility and Output Consistency:
    The sparse transformation changes the structure of the expanded graph, which could affect layout results, node/edge ordering, and even the visual output. The GraphViz team has historically prioritized backward compatibility and reproducibility of layouts, making disruptive algorithmic changes less likely unless there is overwhelming demand.

  3. Lack of Dedicated Maintainer/Champion:
    Implementing the sparse transformation is non-trivial and requires deep understanding of both the original and improved algorithms. There has not been a dedicated maintainer or external contributor willing to champion and maintain such a large change, especially given the risk of subtle bugs and the need for extensive regression testing.

  4. Perceived Complexity vs. Benefit:
    While the sparse transformation offers significant theoretical improvements (especially for large/dense graphs), many real-world dot users work with small-to-medium graphs where the performance bottleneck is less severe. For these users, the existing implementation is "fast enough," reducing the urgency for a disruptive change.

  5. Resource Constraints and Project Priorities:
    The GraphViz project is maintained by a small team with limited resources. Most recent work has focused on bug fixes, build system modernization, and incremental improvements rather than major algorithmic overhauls.

  6. External Workarounds Exist:
    For very large graphs, users are often advised to use alternative engines (e.g., neato, sfdp) or to preprocess graphs (e.g., component splitting) to avoid the worst-case performance scenarios that the sparse transformation would address.

Summary:
The absence of the sparse transformation in GraphViz dot is primarily due to the high engineering cost and risk of integrating it into a legacy codebase, combined with limited resources and a focus on stability and backward compatibility. Theoretical improvements are well-known, but practical adoption has lagged due to these non-technical factors.

If you are interested in advocating for or prototyping this change, it would likely require a dedicated branch, extensive testing, and engagement with the GraphViz maintainers to discuss the path forward.