Heatmaps and consensus clustering for ego network exploration
Bibliographic record
Abstract
Background: Researchers need visualization methods (using statistical and interactive techniques) to efficiently perform quality assessments and glean insights from their data. Data on networks can particularly benefit from more advanced techniques since typical visualization methods, such as node-link diagrams, can be difficult to interpret. We use heatmaps and consensus clustering on network data and show they can be combined to easily and efficiently explore nonparametric relationships among the variables and networks that comprise an ego network data set. Methods: We used ego network data from the Québec Adipose and Lifestyle Investigation in Youth (QUALITY) cohort used to evaluate this method. The data consists of 35 networks centered on individuals (egos), each containing a maximum of 10 nodes (alters). These networks are described through 41 variables: 11 describing the ego (e.g. fat mass percentage), 18 describing the alters (e.g. frequency of physical activity) and 12 describing the network structure (e.g. degree). Results: Four stable clusters were detected. Cluster one consisted of variables relating to the interconnectivity of the ego networks and the locations of interaction, cluster two consisted of the ego’s age, cluster three contained lifestyle variables and obesity outcomes and cluster four was comprised of variables measuring alter importance and diet. Conclusions: This exploratory method using heatmaps and consensus clustering on network data identified several important associations among variables describing the alters’ lifestyle habits and the egos’ obesity outcomes. Their relevance has been identified by studies on the effect of social networks on childhood obesity.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".