On the Performance Analysis of Epidemic Routing in Non-Sparse Delay\n Tolerant Networks
Bibliographic record
Abstract
We study the behavior of epidemic routing in a delay tolerant network as a\nfunction of node density. Focusing on the probability of successful delivery to\na destination within a deadline (PS), we show that PS experiences a phase\ntransition as node density increases. Specifically, we prove that PS exhibits a\nphase transition when nodes are placed according to a Poisson process and\nallowed to move according to independent and identical processes with limited\nspeed. We then propose four fluid models to evaluate the performance of\nepidemic routing in non-sparse networks. A model is proposed for supercritical\nnetworks based on approximation of the infection rate as a function of time.\nOther models are based on the approximation of the pairwise infection rate. Two\nof them, one for subcritical networks and another for supercritical networks,\nuse the pairwise infection rate as a function of the number of infected nodes.\nThe other model uses pairwise infection rate as a function of time, and can be\napplied for both subcritical and supercritical networks achieving good\naccuracy. The model for subcritical networks is accurate when density is not\nclose to the percolation critical density. Moreover, the models that target\nonly supercritical regime are accurate.\n
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".