Map Visualizations for Graphs with Group Restrictions
Bibliographic record
Abstract
A map visualization of a graph consists of a node-link diagram in which groups of nodes are enclosed in one or more polygonal regions, similar to countries in a geographic map. Many real-world graphs have naturally defined groups, e.g., a graph that represents collaborations between faculty members within a university, where the departments are the groups. A good visualization of such a graph should place departments that collaborate frequently as adjacent or nearby groups. While some set visualization methods can be used to create map visualizations for graphs with groups, the results can be poor and difficult to read due to fragmented groups or complicated polygonal shapes of the enclosing regions. With this in mind, we propose a new approach that constructs the polygons first and then renders the graph to obtain better control over the drawing properties. We design two methods based on this new approach and compare them with three prior techniques using seven quantitative metrics on several real-world datasets. Our experimental results demonstrate the proposed methods to outperform prior techniques in capturing the intended drawing features and have good performance in most of the metrics.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".