Epidemic Information Dissemination in Mobile Social Networks With Opportunistic Links
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the advancement of smartphones, mobile social networks (MSNs) have emerged where information can be shared among mobile users via opportunistic peer-to-peer links. Since the social ties and users' behaviors in MSNs have diverse characteristics, the information dissemination in MSNs becomes a new challenge. In particular, mobile users' interested information may vary, which can significantly affect the information dissemination. In this paper, we develop an analytical model to analyze the epidemic information dissemination in MSNs. We first adopt preimmunity and immunity to represent the features of mobile nodes when they change their interests. Then, the information dissemination mechanism is introduced with four proposed dissemination rules according to the process of the epidemic information dissemination. We develop the analytical model through ordinary differential equations to mimic epidemic information dissemination in MSNs. The trace-driven simulation demonstrates that our analytical model is more accurate to mimic epidemic information dissemination than other existing ones.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it