Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
Bibliographic record
Abstract
Graph visualization has been successfully applied in a wide range of problems and applications. Although different approaches are available to create visual representations, most of them suffer from clutter when faced with many nodes and/or edges. Among the techniques that address this problem, edge bundling has attained relative success in improving node-link layouts by bending and aggregating edges. Despite their success, most approaches perform the bundling based only on visual space information. There is no explicit connection between the produced bundled visual representation and the underlying data (edges or vertices attributes). In this paper, we present a novel edge bundling technique, called Similarity-Driven Edge Bundling (SDEB), to address this issue. Our method creates a similarity hierarchy based on a multilevel partition of the data, grouping edges considering the similarity between nodes to guide the bundling. The novel features introduced by SDEB are explored in different application scenarios, from dynamic graph visualization to multilevel exploration. Our results attest that SDEB produces layouts that consistently follow the similarity relationships found in the graph data, resulting in semantically richer presentations that are less cluttered than the state-of-the-art.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".