Bibliographic record
Abstract
Keçeci Layout Mehmet Keçeci ORCID: https://orcid.org/0000-0001-9937-9839, Türkiye Abstract: Keçeci Layout is a deterministic node layout algorithm designed for graph visualization in Python. Its primary purpose is to position the nodes of a graph in a predefined, sequential, and repeatable manner. The algorithm processes nodes sequentially, placing them along a user-defined primary axis (e.g., top-down or left-to-right) while applying an offset on the secondary axis in a zigzag pattern. This zigzag pattern helps prevent node overlaps while maintaining an orderly structure. The function is designed to be compatible with popular Python graph libraries, including NetworkX, Rustworkx, igraph, Networkit, and Graphillion (via GraphSet objects). It takes a graph object from one of these libraries as input, processes the nodes (usually by sorting their IDs), and returns a Python dictionary mapping each node's identifier (in the library-specific format) to its calculated (x, y) coordinates. Users can customize the node spacing along the primary and secondary axes (`primary_spacing`, `secondary_spacing`), the main direction of the layout (`primary_direction`), and the starting side of the zigzag pattern (`secondary_start`) through parameters. This regular and predictable structure is useful, particularly when the order of nodes is significant or when a simple, aesthetically pleasing, and easily traceable graph visualization is desired. Keçeci Layout is a deterministic node layout algorithm designed for graph visualization in Python. Its primary purpose is to position the nodes of a graph in a predefined, sequential, and repeatable manner. The algorithm processes nodes sequentially, placing them along a user-defined primary axis (e.g., top-down or left-to-right) while applying an offset on the secondary axis in a zigzag pattern. This zigzag pattern helps prevent node overlaps while maintaining an orderly structure. The function is designed to be compatible with popular Python graph libraries, including NetworkX, Rustworkx, igraph, Networkit, and Graphillion (via GraphSet objects). It takes a graph object from one of these libraries as input, processes the nodes (usually by sorting their IDs), and returns a Python dictionary mapping each node's identifier (in the library-specific format) to its calculated (x, y) coordinates. Users can customize the node spacing along the primary and secondary axes (`primary_spacing`, `secondary_spacing`), the main direction of the layout (`primary_direction`), and the starting side of the zigzag pattern (`secondary_start`) through parameters. This regular and predictable structure is useful, particularly when the order of nodes is significant or when a simple, aesthetically pleasing, and easily traceable graph visualization is desired. Keywords: Graph Layout, Node Positioning, Zigzag Layout, Sequential Layout, Deterministic Algorithm, Graph Visualization, Keçeci Layout, KececiLayout
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.591 | 0.378 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".