A search-set model of path tracing in graphs
Bibliographic record
Abstract
We present a predictive model of human behaviour when tracing paths through a node-link graph, a low-level abstract task that feeds into many other visual data analysis tasks that require understanding topological structure. We introduce the idea of a search set, namely, the set of paths that users are most likely to search, as a useful intermediate level for analysis that lies between the global level of the full graph and the local level of the shortest path between two nodes. We present potential practical applications of a predicted search set in the design of visual encoding and interaction techniques for graphs. Our predictive model is based on extensive qualitative analysis from an observational study, resulting in a detailed characterization of common path-tracing behaviours. These include the conditions under which people stop following paths, the likely directions for the first hop people follow, the tendency to revisit previously followed paths and the tendency to mistakenly follow apparent paths in addition to true topological paths. The algorithmic implementation of our predictive model is robust to a broad range of parameter settings. We provide a preliminary validation of the model through a hierarchical multiple regression analysis comparing graph readability factors computed on the predicted search set to factors computed at the global level and the local shortest path solution. The tested factors included edge–edge crossings, node–edge crossings, path continuity and path length. Our approach provides modest improvements for predictions of RT and error using search-set factors.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".