Design and Evaluation of Visual Summaries to Improve Readability of Large Network Visualizations
Bibliographic record
Abstract
Node-link visualizations are commonly used to gain insights into large network data where the entities of the networks (nodes) are represented as points, and relationships (edges) are drawn as straight line segments or links. With growing access to network data and visualization tools, such visualizations are increasingly appearing in infographics and documents intended for non-specialist readers. This necessitates understanding how these visualizations are perceived by end users who are not necessarily domain experts, and determining how visual summaries can be provided to ensure consistent interpretation of the displayed information. In this paper, we investigate the interpretability of node-link visualizations of large graphs: we designed summary representations that could be provided alongside the visualization to improve interpretation, and we evaluated these designs through two user studies. Our results indicate that the information perceived from traditional node-link representations can vary substantially, especially when the nodes are uniformly distributed rather than forming clusters or tangled structures. We observed that visual summaries can enhance the readability of these visualizations – summaries that reduce clutter were preferred by participants and were more accurate for typical interpretation tasks.
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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 | 0.014 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".