Exploring personal interest in intermittently connected wireless mobile social networks
Bibliographic record
Abstract
Consumer electronics such as cellular phones and portable computers with short-range communication capabilities have enabled the large-scale information dissemination through user mobility and contact, without the assistance of communication infrastructures. In this new paradigm, one challenge is to determine when and how to forward a message to the destination, possibly through a series of third-party users. This problem has attracted a lot of attention in the literature lately, with proposals ranging from epidemic to single or multi-copy spray and wait or focus strategies, but most existing work assumed independent and identically distributed mobility. Observing most human mobility and interaction are interest-driven in the real world, in this paper, we evaluate the performance of these schemes with an interest-driven mobility model. We further propose to take the user interest into account when determining routing strategies to further improve the performance of these schemes for mobile social networks. Simulation results have demonstrated the efficacy of the interest-aware routing strategies.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".