Predicting human contacts in mobile social networks using supervised learning
Bibliographic record
Abstract
Having access to human contact traces has allowed researchers to study and understand how people contact each other in different social settings. However, most of the existing human contact traces are limited in the number of deployed Bluetooth sensors. In most experiments, there are two types of participants, the ordinary ones who carry cellphones and a specially selected group who additionally carry sensors. Although the contacts between any pair of participants are known when at least one of them carry a sensor, the contacts between any pair of participants are "hidden" when both of them carry their cellphones. In this paper, we employ two well-known supervised classifiers for predicting hidden contacts among participants who carry their cellphones. The performance results of our supervised classifiers show the applicability of using machine learning algorithms for contact prediction task. The results also show that a small subset of features such as number of common neighbors and total overlap time play essential roles in forming human contacts. Finally, we show that contacts of nodes with high centralities are more predictable than nodes with low centralities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".